Overview

Dataset statistics

Number of variables 100
Number of observations 189
Missing cells 6946
Missing cells (%) 36.8%
Duplicate rows 0
Duplicate rows (%) 0.0%
Total size in memory 147.8 KiB
Average record size in memory 800.7 B

Variable types

Categorical 72
Numeric 11
Boolean 14
Unsupported 2

Dataset

Description Chat application experience evaluation questionnaire
Creator Matteo Busso, Massimo Stefan
Author Fausto Giunchiglia, Ivano Bison, Matteo Busso, Ronald Chenu-Abente, Marcelo Rodas Britez, Can Gunel, Giuseppe Veltri, Amalia de Götzen, Peter Kun, Amarsanaa Ganbold, Altangerel Chagnaa, George Gaskell, Miriam Bidoglia, Luca Cernuzzi, Alethia Hume, Jose Luis Zarza, Daniele Miorandi, Carlo Caprini
URL
Copyright (c) KnowDive 2022

Variable descriptions

university University where the experiment took place
id Response ID
submitdate Date submitted
lastpage Last page
startlanguage Start language
seed Seed
token Token
UX01[1] Please indicate whether you agree or disagree with the following statements. [It was easy to install the chatbot]
UX01[2] Please indicate whether you agree or disagree with the following statements. [It was easy to ask a question in the chatbot]
UX01[3] Please indicate whether you agree or disagree with the following statements. [It was easy to provide an answer in the chatbot]
UX01[4] Please indicate whether you agree or disagree with the following statements. [It was easy to decide if I liked an answer]
UX01[5] Please indicate whether you agree or disagree with the following statements. [I had the necessary resources to use the chatbot]
UX01[6] Please indicate whether you agree or disagree with the following statements. [I had the necessary knowledge to use the chatbot]
L01[1] Please select from the options below the one place where you would most likely be on Tuesdays during term time/the semester, at these times of the day. [Before 10:00]
L01[2] Please select from the options below the one place where you would most likely be on Tuesdays during term time/the semester, at these times of the day. [10:00-12:00]
L01[3] Please select from the options below the one place where you would most likely be on Tuesdays during term time/the semester, at these times of the day. [12:00-14:00]
L01[4] Please select from the options below the one place where you would most likely be on Tuesdays during term time/the semester, at these times of the day. [14:00-16:00]
L01[5] Please select from the options below the one place where you would most likely be on Tuesdays during term time/the semester, at these times of the day. [16:00-18:00]
L01[6] Please select from the options below the one place where you would most likely be on Tuesdays during term time/the semester, at these times of the day. [After 18:00]
L02[1] Please select from the options below the one place where you would most likely be on Saturdays during term time/the semester, at these times of the day. [Before 10:00]
L02[2] Please select from the options below the one place where you would most likely be on Saturdays during term time/the semester, at these times of the day. [10:00-12:00]
L02[3] Please select from the options below the one place where you would most likely be on Saturdays during term time/the semester, at these times of the day. [12:00-14:00]
L02[4] Please select from the options below the one place where you would most likely be on Saturdays during term time/the semester, at these times of the day. [14:00-16:00]
L02[5] Please select from the options below the one place where you would most likely be on Saturdays during term time/the semester, at these times of the day. [16:00-18:00]
L02[6] Please select from the options below the one place where you would most likely be on Saturdays during term time/the semester, at these times of the day. [After 18:00]
L03[1] Please select from the options below the one place where you would most likely be on Thursdays during term time/the semester, at these times of the day. [Before 10:00]
L03[2] Please select from the options below the one place where you would most likely be on Thursdays during term time/the semester, at these times of the day. [10:00-12:00]
L03[3] Please select from the options below the one place where you would most likely be on Thursdays during term time/the semester, at these times of the day. [12:00-14:00]
L03[4] Please select from the options below the one place where you would most likely be on Thursdays during term time/the semester, at these times of the day. [14:00-16:00]
L03[5] Please select from the options below the one place where you would most likely be on Thursdays during term time/the semester, at these times of the day. [16:00-18:00]
L03[6] Please select from the options below the one place where you would most likely be on Thursdays during term time/the semester, at these times of the day. [After 18:00]
L04[1] Please select from the options below the one place where you would most likely be on Sundays during term time/the semester, at these times of the day. [Before 10:00]
L04[2] Please select from the options below the one place where you would most likely be on Sundays during term time/the semester, at these times of the day. [10:00-12:00]
L04[3] Please select from the options below the one place where you would most likely be on Sundays during term time/the semester, at these times of the day. [12:00-14:00]
L04[4] Please select from the options below the one place where you would most likely be on Sundays during term time/the semester, at these times of the day. [14:00-16:00]
L04[5] Please select from the options below the one place where you would most likely be on Sundays during term time/the semester, at these times of the day. [16:00-18:00]
L04[6] Please select from the options below the one place where you would most likely be on Sundays during term time/the semester, at these times of the day. [After 18:00]
TS01[1] Was there a particular time of day when it was useful to put questions into the chatbot? [In the morning]
TS01[2] Was there a particular time of day when it was useful to put questions into the chatbot? [Around noon]
TS01[3] Was there a particular time of day when it was useful to put questions into the chatbot? [In the afternoon]
TS01[4] Was there a particular time of day when it was useful to put questions into the chatbot? [In the evening]
TS01[5] Was there a particular time of day when it was useful to put questions into the chatbot? [At night]
TS02[1] From which locations was it convenient to put questions to the chatbot? [At home]
TS02[2] From which locations was it convenient to put questions to the chatbot? [At someone else's home]
TS02[3] From which locations was it convenient to put questions to the chatbot? [On the Uni campus]
TS02[4] From which locations was it convenient to put questions to the chatbot? [At work other than Uni]
TS02[5] From which locations was it convenient to put questions to the chatbot? [Café/restaurant]
TS02[6] From which locations was it convenient to put questions to the chatbot? [Public outdoor place (not Uni)]
TS02[7] From which locations was it convenient to put questions to the chatbot? [Public indoor place (not Uni)]
TS02[8] From which locations was it convenient to put questions to the chatbot? [On the go]
TS02[9] From which locations was it convenient to put questions to the chatbot? [Other places]
B01 Here is an example badge: “Congratulations! You just earned the First Question badge! Way to go!”
B02 Please write in at least one badge you received
B03[1] Please indicate whether you agree or disagree with these statements [I liked the chatbot's badges]
B03[2] Please indicate whether you agree or disagree with these statements [The badges were a distraction]
B03[3] Please indicate whether you agree or disagree with these statements [The badges enhanced the chatbot experience]
B03[4] Please indicate whether you agree or disagree with these statements [The badges encouraged me to contribute to the chatbot]
B03[5] Please indicate whether you agree or disagree with these statements [Chatbot should be more generous with badges]
B03[6] Please indicate whether you agree or disagree with these statements [More type of badges should be used]
B03[7] Please indicate whether you agree or disagree with these statements [Badges based on the acceptance of answers should be used more]
M01 Here is an example message: “You haven't asked a question yet. You can get help from the community with your questions. Type /question to ask the community!”
M02 Please write in at least one message you received.
M03[1] Please indicate whether you agree or disagree with these statements. [I liked the chatbot’s messages]
M03[2] Please indicate whether you agree or disagree with these statements. [The messages enhanced the chatbot experience]
M03[3] Please indicate whether you agree or disagree with these statements. [The messages were a distraction]
M03[4] Please indicate whether you agree or disagree with these statements. [The messages encouraged me to contribute to chatbot]
M03[5] Please indicate whether you agree or disagree with these statements. [More types of messages should be used]
M03[6] Please indicate whether you agree or disagree with these statements. [Messages should be sent less frequently]
M03[7] Please indicate whether you agree or disagree with these statements. [Messages should be personalised for each user]
UX02[1] Please indicate whether you agree or disagree with the following statements. [The chatbot helped me to acquire new ideas]
UX02[2] Please indicate whether you agree or disagree with the following statements. [The chatbot was useful to reach out for help ]
UX02[3] Please indicate whether you agree or disagree with the following statements. [The chatbot was useful to provide help to others.]
UX02[4] Please indicate whether you agree or disagree with the following statements. [I found the chatbot useful to get to know other students ]
UX02[5] Please indicate whether you agree or disagree with the following statements. [I found the chatbot useful to make me feel part of a community]
UX02[6] Please indicate whether you agree or disagree with the following statements. [I felt comfortable using the chatbot to ask questions]
UX02[7] Please indicate whether you agree or disagree with the following statements. [I felt comfortable using the chatbot to answer questions]
UX02[8] Please indicate whether you agree or disagree with the following statements. [I felt pleased to be able to provide an answer]
UX02[9] Please indicate whether you agree or disagree with the following statements. [I felt pleased to get answers to my questions]
UX02[10] Please indicate whether you agree or disagree with the following statements. [The chatbot had an appealing tone of voice]
UX02[11] Please indicate whether you agree or disagree with the following statements. [I found the chatbot trustworthy ]
UX02[12] Please indicate whether you agree or disagree with the following statements. [Using the chatbot was rewarding]
UX02[13] Please indicate whether you agree or disagree with the following statements. [Using the chatbot was fun]
UX02[14] Please indicate whether you agree or disagree with the following statements. [I was interested in the experience of chatbot ]
UX02[15] Please indicate whether you agree or disagree with the following statements. [I would keep using the chatbot in my everyday life]
UX02[16] Please indicate whether you agree or disagree with the following statements. [I use other chatbots in my everyday life]
F01 Thanks for completing the questionnaire! Please click on the "Submit" button to finalize your answers. If you have any other comments on the chatbot, we would be pleased to read them.
attribute 1
startdate Date started
datestamp Date last action
ipaddr IP address
refurl Referrer URL
interviewtime Total time
groupTime388 Group time: User experience - part 1
groupTime382 Group time: Location
groupTime383 Group time: Time and space of questions
groupTime384 Group time: Badges
groupTime385 Group time: Messages
groupTime386 Group time: User experience - part 2
groupTime387 Group time: Final question

Alerts

submitdate has a high cardinality: 79 distinct values High cardinality
token has a high cardinality: 176 distinct values High cardinality
B02 has a high cardinality: 146 distinct values High cardinality
F01 has a high cardinality: 75 distinct values High cardinality
startdate has a high cardinality: 95 distinct values High cardinality
datestamp has a high cardinality: 95 distinct values High cardinality
ipaddr has a high cardinality: 86 distinct values High cardinality
interviewtime is highly correlated with groupTime383 and 3 other fields High correlation
groupTime383 is highly correlated with interviewtime High correlation
groupTime384 is highly correlated with interviewtime High correlation
groupTime385 is highly correlated with interviewtime High correlation
groupTime387 is highly correlated with interviewtime High correlation
lastpage is highly correlated with groupTime384 High correlation
interviewtime is highly correlated with groupTime384 and 2 other fields High correlation
groupTime384 is highly correlated with lastpage and 1 other fields High correlation
groupTime385 is highly correlated with interviewtime High correlation
groupTime387 is highly correlated with interviewtime High correlation
university is highly correlated with startlanguage and 23 other fields High correlation
id is highly correlated with submitdate and 7 other fields High correlation
submitdate is highly correlated with id and 47 other fields High correlation
lastpage is highly correlated with UX01[1] and 14 other fields High correlation
startlanguage is highly correlated with university and 22 other fields High correlation
seed is highly correlated with M02 and 6 other fields High correlation
UX01[1] is highly correlated with lastpage and 14 other fields High correlation
UX01[2] is highly correlated with submitdate and 10 other fields High correlation
UX01[3] is highly correlated with UX01[1] and 12 other fields High correlation
UX01[4] is highly correlated with submitdate and 21 other fields High correlation
UX01[5] is highly correlated with submitdate and 22 other fields High correlation
UX01[6] is highly correlated with submitdate and 15 other fields High correlation
L01[1] is highly correlated with submitdate and 11 other fields High correlation
L01[2] is highly correlated with L01[1] and 9 other fields High correlation
L01[3] is highly correlated with submitdate and 13 other fields High correlation
L01[4] is highly correlated with submitdate and 14 other fields High correlation
L01[5] is highly correlated with UX01[5] and 14 other fields High correlation
L01[6] is highly correlated with submitdate and 17 other fields High correlation
L02[1] is highly correlated with id and 14 other fields High correlation
L02[2] is highly correlated with submitdate and 12 other fields High correlation
L02[3] is highly correlated with L01[2] and 11 other fields High correlation
L02[4] is highly correlated with submitdate and 12 other fields High correlation
L02[5] is highly correlated with submitdate and 12 other fields High correlation
L02[6] is highly correlated with submitdate and 13 other fields High correlation
L03[1] is highly correlated with L03[2] and 12 other fields High correlation
L03[2] is highly correlated with L03[1] and 13 other fields High correlation
L03[3] is highly correlated with UX01[4] and 18 other fields High correlation
L03[4] is highly correlated with submitdate and 20 other fields High correlation
L03[5] is highly correlated with submitdate and 17 other fields High correlation
L03[6] is highly correlated with L03[4] and 10 other fields High correlation
L04[1] is highly correlated with L03[1] and 15 other fields High correlation
L04[2] is highly correlated with L03[6] and 11 other fields High correlation
L04[3] is highly correlated with L03[6] and 13 other fields High correlation
L04[4] is highly correlated with submitdate and 17 other fields High correlation
L04[5] is highly correlated with L04[1] and 10 other fields High correlation
L04[6] is highly correlated with L03[4] and 13 other fields High correlation
TS01[1] is highly correlated with M02 and 5 other fields High correlation
TS01[2] is highly correlated with submitdate and 6 other fields High correlation
TS01[3] is highly correlated with L03[4] and 6 other fields High correlation
TS01[4] is highly correlated with F01 and 3 other fields High correlation
TS01[5] is highly correlated with startlanguage and 7 other fields High correlation
TS02[1] is highly correlated with submitdate and 10 other fields High correlation
TS02[2] is highly correlated with L03[5] and 7 other fields High correlation
TS02[3] is highly correlated with submitdate and 6 other fields High correlation
TS02[4] is highly correlated with L01[1] and 10 other fields High correlation
TS02[5] is highly correlated with L02[6] and 9 other fields High correlation
TS02[6] is highly correlated with submitdate and 7 other fields High correlation
TS02[7] is highly correlated with TS02[4] and 6 other fields High correlation
TS02[8] is highly correlated with F01 and 2 other fields High correlation
TS02[9] is highly correlated with L03[1] and 4 other fields High correlation
B03[1] is highly correlated with submitdate and 17 other fields High correlation
B03[2] is highly correlated with submitdate and 13 other fields High correlation
B03[3] is highly correlated with submitdate and 15 other fields High correlation
B03[4] is highly correlated with submitdate and 15 other fields High correlation
B03[5] is highly correlated with submitdate and 17 other fields High correlation
B03[6] is highly correlated with submitdate and 19 other fields High correlation
B03[7] is highly correlated with submitdate and 16 other fields High correlation
M02 is highly correlated with university and 57 other fields High correlation
M03[1] is highly correlated with submitdate and 36 other fields High correlation
M03[2] is highly correlated with submitdate and 31 other fields High correlation
M03[3] is highly correlated with UX01[6] and 14 other fields High correlation
M03[4] is highly correlated with seed and 26 other fields High correlation
M03[5] is highly correlated with submitdate and 30 other fields High correlation
M03[6] is highly correlated with submitdate and 25 other fields High correlation
M03[7] is highly correlated with submitdate and 24 other fields High correlation
UX02[1] is highly correlated with university and 27 other fields High correlation
UX02[2] is highly correlated with university and 27 other fields High correlation
UX02[3] is highly correlated with university and 29 other fields High correlation
UX02[4] is highly correlated with university and 23 other fields High correlation
UX02[5] is highly correlated with university and 27 other fields High correlation
UX02[6] is highly correlated with university and 25 other fields High correlation
UX02[7] is highly correlated with university and 27 other fields High correlation
UX02[8] is highly correlated with university and 34 other fields High correlation
UX02[9] is highly correlated with university and 31 other fields High correlation
UX02[10] is highly correlated with university and 28 other fields High correlation
UX02[11] is highly correlated with university and 26 other fields High correlation
UX02[12] is highly correlated with university and 26 other fields High correlation
UX02[13] is highly correlated with university and 27 other fields High correlation
UX02[14] is highly correlated with university and 26 other fields High correlation
UX02[15] is highly correlated with university and 22 other fields High correlation
UX02[16] is highly correlated with university and 26 other fields High correlation
F01 is highly correlated with university and 91 other fields High correlation
attribute is highly correlated with university and 6 other fields High correlation
startdate is highly correlated with university and 92 other fields High correlation
datestamp is highly correlated with university and 92 other fields High correlation
ipaddr is highly correlated with university and 79 other fields High correlation
refurl is highly correlated with university and 25 other fields High correlation
interviewtime is highly correlated with submitdate and 10 other fields High correlation
groupTime388 is highly correlated with submitdate and 10 other fields High correlation
groupTime382 is highly correlated with submitdate and 14 other fields High correlation
groupTime383 is highly correlated with submitdate and 6 other fields High correlation
groupTime384 is highly correlated with submitdate and 7 other fields High correlation
groupTime385 is highly correlated with submitdate and 18 other fields High correlation
groupTime386 is highly correlated with submitdate and 13 other fields High correlation
groupTime387 is highly correlated with submitdate and 12 other fields High correlation
submitdate has 22 (11.6%) missing values Missing
lastpage has 9 (4.8%) missing values Missing
UX01[1] has 11 (5.8%) missing values Missing
UX01[2] has 11 (5.8%) missing values Missing
UX01[3] has 11 (5.8%) missing values Missing
UX01[4] has 11 (5.8%) missing values Missing
UX01[5] has 11 (5.8%) missing values Missing
UX01[6] has 11 (5.8%) missing values Missing
L01[1] has 134 (70.9%) missing values Missing
L01[2] has 134 (70.9%) missing values Missing
L01[3] has 134 (70.9%) missing values Missing
L01[4] has 134 (70.9%) missing values Missing
L01[5] has 134 (70.9%) missing values Missing
L01[6] has 134 (70.9%) missing values Missing
L02[1] has 134 (70.9%) missing values Missing
L02[2] has 134 (70.9%) missing values Missing
L02[3] has 134 (70.9%) missing values Missing
L02[4] has 134 (70.9%) missing values Missing
L02[5] has 134 (70.9%) missing values Missing
L02[6] has 134 (70.9%) missing values Missing
L03[1] has 131 (69.3%) missing values Missing
L03[2] has 131 (69.3%) missing values Missing
L03[3] has 131 (69.3%) missing values Missing
L03[4] has 131 (69.3%) missing values Missing
L03[5] has 131 (69.3%) missing values Missing
L03[6] has 131 (69.3%) missing values Missing
L04[1] has 131 (69.3%) missing values Missing
L04[2] has 131 (69.3%) missing values Missing
L04[3] has 131 (69.3%) missing values Missing
L04[4] has 131 (69.3%) missing values Missing
L04[5] has 131 (69.3%) missing values Missing
L04[6] has 131 (69.3%) missing values Missing
TS01[1] has 15 (7.9%) missing values Missing
TS01[2] has 15 (7.9%) missing values Missing
TS01[3] has 15 (7.9%) missing values Missing
TS01[4] has 15 (7.9%) missing values Missing
TS01[5] has 15 (7.9%) missing values Missing
TS02[1] has 15 (7.9%) missing values Missing
TS02[2] has 15 (7.9%) missing values Missing
TS02[3] has 15 (7.9%) missing values Missing
TS02[4] has 15 (7.9%) missing values Missing
TS02[5] has 15 (7.9%) missing values Missing
TS02[6] has 15 (7.9%) missing values Missing
TS02[7] has 15 (7.9%) missing values Missing
TS02[8] has 15 (7.9%) missing values Missing
TS02[9] has 15 (7.9%) missing values Missing
B01 has 189 (100.0%) missing values Missing
B02 has 20 (10.6%) missing values Missing
B03[1] has 19 (10.1%) missing values Missing
B03[2] has 19 (10.1%) missing values Missing
B03[3] has 19 (10.1%) missing values Missing
B03[4] has 19 (10.1%) missing values Missing
B03[5] has 19 (10.1%) missing values Missing
B03[6] has 19 (10.1%) missing values Missing
B03[7] has 19 (10.1%) missing values Missing
M01 has 189 (100.0%) missing values Missing
M02 has 135 (71.4%) missing values Missing
M03[1] has 135 (71.4%) missing values Missing
M03[2] has 135 (71.4%) missing values Missing
M03[3] has 135 (71.4%) missing values Missing
M03[4] has 135 (71.4%) missing values Missing
M03[5] has 135 (71.4%) missing values Missing
M03[6] has 135 (71.4%) missing values Missing
M03[7] has 135 (71.4%) missing values Missing
UX02[1] has 21 (11.1%) missing values Missing
UX02[2] has 21 (11.1%) missing values Missing
UX02[3] has 21 (11.1%) missing values Missing
UX02[4] has 21 (11.1%) missing values Missing
UX02[5] has 21 (11.1%) missing values Missing
UX02[6] has 21 (11.1%) missing values Missing
UX02[7] has 21 (11.1%) missing values Missing
UX02[8] has 21 (11.1%) missing values Missing
UX02[9] has 21 (11.1%) missing values Missing
UX02[10] has 21 (11.1%) missing values Missing
UX02[11] has 21 (11.1%) missing values Missing
UX02[12] has 21 (11.1%) missing values Missing
UX02[13] has 21 (11.1%) missing values Missing
UX02[14] has 21 (11.1%) missing values Missing
UX02[15] has 21 (11.1%) missing values Missing
UX02[16] has 21 (11.1%) missing values Missing
F01 has 114 (60.3%) missing values Missing
attribute has 64 (33.9%) missing values Missing
startdate has 94 (49.7%) missing values Missing
datestamp has 94 (49.7%) missing values Missing
ipaddr has 94 (49.7%) missing values Missing
refurl has 166 (87.8%) missing values Missing
interviewtime has 94 (49.7%) missing values Missing
groupTime388 has 101 (53.4%) missing values Missing
groupTime382 has 106 (56.1%) missing values Missing
groupTime383 has 106 (56.1%) missing values Missing
groupTime384 has 108 (57.1%) missing values Missing
groupTime385 has 150 (79.4%) missing values Missing
groupTime386 has 110 (58.2%) missing values Missing
groupTime387 has 111 (58.7%) missing values Missing
token is uniformly distributed Uniform
B02 is uniformly distributed Uniform
M02 is uniformly distributed Uniform
F01 is uniformly distributed Uniform
startdate is uniformly distributed Uniform
datestamp is uniformly distributed Uniform
ipaddr is uniformly distributed Uniform
seed has unique values Unique
B01 is an unsupported type, check if it needs cleaning or further analysis Unsupported
M01 is an unsupported type, check if it needs cleaning or further analysis Unsupported
lastpage has 2 (1.1%) zeros Zeros
interviewtime has 7 (3.7%) zeros Zeros

Reproduction

Analysis started 2022-07-04 18:13:08.188668
Analysis finished 2022-07-04 18:14:15.359749
Duration 1 minute and 7.17 seconds
Software version pandas-profiling v3.2.0
Download configuration config.json

Variables

university
Categorical

HIGH CORRELATION

University where the experiment took place

Distinct 5
Distinct (%) 2.6%
Missing 0
Missing (%) 0.0%
Memory size 1.6 KiB
LSE
51
UNITN
44
NUM
43
AAU
30
UC
21

Length

Max length 5
Median length 3
Mean length 3.354497354
Min length 2

Characters and Unicode

Total characters 634
Distinct characters 10
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row UC
2nd row UC
3rd row UC
4th row UC
5th row UC

Common Values

Value Count Frequency (%)
LSE 51
27.0%
UNITN 44
23.3%
NUM 43
22.8%
AAU 30
15.9%
UC 21
11.1%

Length

2022-07-04T20:14:15.495781 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:15.759526 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
lse 51
27.0%
unitn 44
23.3%
num 43
22.8%
aau 30
15.9%
uc 21
11.1%

Most occurring characters

Value Count Frequency (%)
U 138
21.8%
N 131
20.7%
A 60
9.5%
L 51
8.0%
S 51
8.0%
E 51
8.0%
I 44
6.9%
T 44
6.9%
M 43
6.8%
C 21
3.3%

Most occurring categories

Value Count Frequency (%)
Uppercase Letter 634
100.0%

Most frequent character per category

Uppercase Letter
Value Count Frequency (%)
U 138
21.8%
N 131
20.7%
A 60
9.5%
L 51
8.0%
S 51
8.0%
E 51
8.0%
I 44
6.9%
T 44
6.9%
M 43
6.8%
C 21
3.3%

Most occurring scripts

Value Count Frequency (%)
Latin 634
100.0%

Most frequent character per script

Latin
Value Count Frequency (%)
U 138
21.8%
N 131
20.7%
A 60
9.5%
L 51
8.0%
S 51
8.0%
E 51
8.0%
I 44
6.9%
T 44
6.9%
M 43
6.8%
C 21
3.3%

Most occurring blocks

Value Count Frequency (%)
ASCII 634
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
U 138
21.8%
N 131
20.7%
A 60
9.5%
L 51
8.0%
S 51
8.0%
E 51
8.0%
I 44
6.9%
T 44
6.9%
M 43
6.8%
C 21
3.3%

id
Real number (ℝ ≥0 )

HIGH CORRELATION

Response ID

Distinct 52
Distinct (%) 27.5%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 31.60846561
Minimum 10
Maximum 61
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 1.6 KiB
2022-07-04T20:14:16.027418 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum 10
5-th percentile 12
Q1 20
median 30
Q3 42
95-th percentile 55.6
Maximum 61
Range 51
Interquartile range (IQR) 22

Descriptive statistics

Standard deviation 13.57740349
Coefficient of variation (CV) 0.4295495913
Kurtosis -0.9222240153
Mean 31.60846561
Median Absolute Deviation (MAD) 11
Skewness 0.3304091601
Sum 5974
Variance 184.3458854
Monotonicity Not monotonic
2022-07-04T20:14:16.322843 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
28 5
2.6%
20 5
2.6%
30 5
2.6%
29 5
2.6%
27 5
2.6%
26 5
2.6%
25 5
2.6%
24 5
2.6%
23 5
2.6%
22 5
2.6%
Other values (42) 139
73.5%
Value Count Frequency (%)
10 3
1.6%
11 4
2.1%
12 4
2.1%
13 4
2.1%
14 4
2.1%
15 4
2.1%
16 5
2.6%
17 5
2.6%
18 5
2.6%
19 5
2.6%
Value Count Frequency (%)
61 1
0.5%
60 1
0.5%
59 2
1.1%
58 2
1.1%
57 2
1.1%
56 2
1.1%
55 2
1.1%
54 2
1.1%
53 2
1.1%
52 3
1.6%

submitdate
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Date submitted

Distinct 79
Distinct (%) 47.3%
Missing 22
Missing (%) 11.6%
Memory size 1.6 KiB
1980-01-01 00:00:00
89
2021-03-28 09:21:45
1
2021-03-28 19:18:55
1
2021-03-28 19:02:32
1
2021-03-28 18:57:14
1
Other values (74)
74

Length

Max length 19
Median length 19
Mean length 19
Min length 19

Characters and Unicode

Total characters 3173
Distinct characters 13
Distinct categories 4 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 78 ?
Unique (%) 46.7%

Sample

1st row 1980-01-01 00:00:00
2nd row 1980-01-01 00:00:00
3rd row 1980-01-01 00:00:00
4th row 1980-01-01 00:00:00
5th row 1980-01-01 00:00:00

Common Values

Value Count Frequency (%)
1980-01-01 00:00:00 89
47.1%
2021-03-28 09:21:45 1
0.5%
2021-03-28 19:18:55 1
0.5%
2021-03-28 19:02:32 1
0.5%
2021-03-28 18:57:14 1
0.5%
2021-03-28 16:47:42 1
0.5%
2021-03-28 15:19:35 1
0.5%
2021-03-28 14:43:33 1
0.5%
2021-03-28 13:55:41 1
0.5%
2021-03-27 21:45:09 1
0.5%
Other values (69) 69
36.5%
(Missing) 22
11.6%

Length

2022-07-04T20:14:16.579642 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
Value Count Frequency (%)
1980-01-01 89
26.6%
00:00:00 89
26.6%
2021-06-18 29
8.7%
2021-03-29 11
3.3%
2021-03-28 10
3.0%
2021-03-27 10
3.0%
2021-06-28 5
1.5%
2021-03-31 5
1.5%
2021-06-30 2
0.6%
11:33:54 1
0.3%
Other values (83) 83
24.9%

Most occurring characters

Value Count Frequency (%)
0 1006
31.7%
1 515
16.2%
- 334
10.5%
: 334
10.5%
2 252
7.9%
167
5.3%
8 151
4.8%
9 121
3.8%
3 102
3.2%
6 58
1.8%
Other values (3) 133
4.2%

Most occurring categories

Value Count Frequency (%)
Decimal Number 2338
73.7%
Dash Punctuation 334
10.5%
Other Punctuation 334
10.5%
Space Separator 167
5.3%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
0 1006
43.0%
1 515
22.0%
2 252
10.8%
8 151
6.5%
9 121
5.2%
3 102
4.4%
6 58
2.5%
5 52
2.2%
4 51
2.2%
7 30
1.3%
Dash Punctuation
Value Count Frequency (%)
- 334
100.0%
Other Punctuation
Value Count Frequency (%)
: 334
100.0%
Space Separator
Value Count Frequency (%)
167
100.0%

Most occurring scripts

Value Count Frequency (%)
Common 3173
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
0 1006
31.7%
1 515
16.2%
- 334
10.5%
: 334
10.5%
2 252
7.9%
167
5.3%
8 151
4.8%
9 121
3.8%
3 102
3.2%
6 58
1.8%
Other values (3) 133
4.2%

Most occurring blocks

Value Count Frequency (%)
ASCII 3173
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
0 1006
31.7%
1 515
16.2%
- 334
10.5%
: 334
10.5%
2 252
7.9%
167
5.3%
8 151
4.8%
9 121
3.8%
3 102
3.2%
6 58
1.8%
Other values (3) 133
4.2%

lastpage
Real number (ℝ ≥0 )

HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Last page

Distinct 7
Distinct (%) 3.9%
Missing 9
Missing (%) 4.8%
Infinite 0
Infinite (%) 0.0%
Mean 6.661111111
Minimum 0
Maximum 7
Zeros 2
Zeros (%) 1.1%
Negative 0
Negative (%) 0.0%
Memory size 1.6 KiB
2022-07-04T20:14:16.776920 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum 0
5-th percentile 3
Q1 7
median 7
Q3 7
95-th percentile 7
Maximum 7
Range 7
Interquartile range (IQR) 0

Descriptive statistics

Standard deviation 1.295302596
Coefficient of variation (CV) 0.1944574372
Kurtosis 14.2728771
Mean 6.661111111
Median Absolute Deviation (MAD) 0
Skewness -3.88533542
Sum 1199
Variance 1.677808814
Monotonicity Not monotonic
2022-07-04T20:14:16.962635 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
Value Count Frequency (%)
7 167
88.4%
3 5
2.6%
1 3
1.6%
0 2
1.1%
6 1
0.5%
4 1
0.5%
2 1
0.5%
(Missing) 9
4.8%
Value Count Frequency (%)
0 2
1.1%
1 3
1.6%
2 1
0.5%
3 5
2.6%
4 1
0.5%
6 1
0.5%
7 167
88.4%
Value Count Frequency (%)
7 167
88.4%
6 1
0.5%
4 1
0.5%
3 5
2.6%
2 1
0.5%
1 3
1.6%
0 2
1.1%

startlanguage
Categorical

HIGH CORRELATION

Start language

Distinct 4
Distinct (%) 2.1%
Missing 0
Missing (%) 0.0%
Memory size 1.6 KiB
en
81
it
44
mn
43
es
21

Length

Max length 2
Median length 2
Mean length 2
Min length 2

Characters and Unicode

Total characters 378
Distinct characters 6
Distinct categories 1 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row es
2nd row es
3rd row es
4th row es
5th row es

Common Values

Value Count Frequency (%)
en 81
42.9%
it 44
23.3%
mn 43
22.8%
es 21
11.1%

Length

2022-07-04T20:14:17.187509 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:17.429600 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
en 81
42.9%
it 44
23.3%
mn 43
22.8%
es 21
11.1%

Most occurring characters

Value Count Frequency (%)
n 124
32.8%
e 102
27.0%
i 44
11.6%
t 44
11.6%
m 43
11.4%
s 21
5.6%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 378
100.0%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
n 124
32.8%
e 102
27.0%
i 44
11.6%
t 44
11.6%
m 43
11.4%
s 21
5.6%

Most occurring scripts

Value Count Frequency (%)
Latin 378
100.0%

Most frequent character per script

Latin
Value Count Frequency (%)
n 124
32.8%
e 102
27.0%
i 44
11.6%
t 44
11.6%
m 43
11.4%
s 21
5.6%

Most occurring blocks

Value Count Frequency (%)
ASCII 378
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
n 124
32.8%
e 102
27.0%
i 44
11.6%
t 44
11.6%
m 43
11.4%
s 21
5.6%

seed
Real number (ℝ ≥0 )

HIGH CORRELATION
UNIQUE

Seed

Distinct 189
Distinct (%) 100.0%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 1131734767
Minimum 9404622
Maximum 2147220493
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 1.6 KiB
2022-07-04T20:14:17.687582 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum 9404622
5-th percentile 169071459.8
Q1 632514094
median 1158871788
Q3 1624025464
95-th percentile 2041875206
Maximum 2147220493
Range 2137815871
Interquartile range (IQR) 991511370

Descriptive statistics

Standard deviation 603663951.1
Coefficient of variation (CV) 0.5333970189
Kurtosis -1.140570627
Mean 1131734767
Median Absolute Deviation (MAD) 489738408
Skewness -0.1537781819
Sum 2.13897871 × 10 11
Variance 3.644101658 × 10 17
Monotonicity Not monotonic
2022-07-04T20:14:17.966000 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
250605228 1
0.5%
1273651840 1
0.5%
1261905285 1
0.5%
594295559 1
0.5%
964035441 1
0.5%
1262043265 1
0.5%
1263055257 1
0.5%
2014031907 1
0.5%
2048261442 1
0.5%
464171009 1
0.5%
Other values (179) 179
94.7%
Value Count Frequency (%)
9404622 1
0.5%
26414604 1
0.5%
27351635 1
0.5%
63623703 1
0.5%
64687613 1
0.5%
77329382 1
0.5%
111421394 1
0.5%
129634584 1
0.5%
157057916 1
0.5%
159128867 1
0.5%
Value Count Frequency (%)
2147220493 1
0.5%
2125964983 1
0.5%
2093641139 1
0.5%
2066468948 1
0.5%
2063284277 1
0.5%
2060516412 1
0.5%
2060303211 1
0.5%
2052951351 1
0.5%
2048261442 1
0.5%
2046913007 1
0.5%

token
Categorical

HIGH CARDINALITY
UNIFORM

Token

Distinct 176
Distinct (%) 93.1%
Missing 0
Missing (%) 0.0%
Memory size 1.6 KiB
jMzQbgw6fta2eOp
4
vG7c8fhJtwtJBeO
2
S64U1QjIa1Wx6zQ
2
7mDkSBKScUe9FLt
2
4a90FkkijIRBPP2
2
Other values (171)
177

Length

Max length 15
Median length 15
Mean length 15
Min length 15

Characters and Unicode

Total characters 2835
Distinct characters 62
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 165 ?
Unique (%) 87.3%

Sample

1st row jsW2yz4VdiC3FGH
2nd row raql9DLn47TvGW7
3rd row qh5FIDn07bOevCw
4th row MKoLz9TTcPw2ipz
5th row Idygy0rkdVzGNxu

Common Values

Value Count Frequency (%)
jMzQbgw6fta2eOp 4
2.1%
vG7c8fhJtwtJBeO 2
1.1%
S64U1QjIa1Wx6zQ 2
1.1%
7mDkSBKScUe9FLt 2
1.1%
4a90FkkijIRBPP2 2
1.1%
wiyB67HLD9vCRX0 2
1.1%
hfPvA62MpGKGRro 2
1.1%
BBbUtG3qOYDVEtu 2
1.1%
0fb9gVGv21uxpSQ 2
1.1%
J8WxOavUisUTBgb 2
1.1%
Other values (166) 167
88.4%

Length

2022-07-04T20:14:18.225740 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
Value Count Frequency (%)
jmzqbgw6fta2eop 4
2.1%
hfpva62mpgkgrro 2
1.1%
vg7c8fhjtwtjbeo 2
1.1%
j8wxoavuisutbgb 2
1.1%
0fb9gvgv21uxpsq 2
1.1%
bbbutg3qoydvetu 2
1.1%
qp2oti3vgjahlzt 2
1.1%
wiyb67hld9vcrx0 2
1.1%
4a90fkkijirbpp2 2
1.1%
7mdksbkscue9flt 2
1.1%
Other values (166) 167
88.4%

Most occurring characters

Value Count Frequency (%)
a 90
3.2%
z 82
2.9%
J 57
2.0%
4 55
1.9%
C 54
1.9%
v 54
1.9%
i 53
1.9%
Q 53
1.9%
f 53
1.9%
h 52
1.8%
Other values (52) 2232
78.7%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1222
43.1%
Uppercase Letter 1157
40.8%
Decimal Number 456
16.1%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
a 90
7.4%
z 82
6.7%
v 54
4.4%
i 53
4.3%
f 53
4.3%
h 52
4.3%
d 51
4.2%
g 50
4.1%
m 46
3.8%
j 46
3.8%
Other values (16) 645
52.8%
Uppercase Letter
Value Count Frequency (%)
J 57
4.9%
C 54
4.7%
Q 53
4.6%
A 52
4.5%
H 52
4.5%
U 51
4.4%
T 49
4.2%
G 48
4.1%
S 48
4.1%
B 47
4.1%
Other values (16) 646
55.8%
Decimal Number
Value Count Frequency (%)
4 55
12.1%
2 49
10.7%
0 48
10.5%
1 48
10.5%
7 47
10.3%
6 46
10.1%
9 44
9.6%
3 44
9.6%
5 39
8.6%
8 36
7.9%

Most occurring scripts

Value Count Frequency (%)
Latin 2379
83.9%
Common 456
16.1%

Most frequent character per script

Latin
Value Count Frequency (%)
a 90
3.8%
z 82
3.4%
J 57
2.4%
C 54
2.3%
v 54
2.3%
i 53
2.2%
Q 53
2.2%
f 53
2.2%
h 52
2.2%
A 52
2.2%
Other values (42) 1779
74.8%
Common
Value Count Frequency (%)
4 55
12.1%
2 49
10.7%
0 48
10.5%
1 48
10.5%
7 47
10.3%
6 46
10.1%
9 44
9.6%
3 44
9.6%
5 39
8.6%
8 36
7.9%

Most occurring blocks

Value Count Frequency (%)
ASCII 2835
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
a 90
3.2%
z 82
2.9%
J 57
2.0%
4 55
1.9%
C 54
1.9%
v 54
1.9%
i 53
1.9%
Q 53
1.9%
f 53
1.9%
h 52
1.8%
Other values (52) 2232
78.7%

UX01[1]
Categorical

HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with the following statements. [It was easy to install the chatbot]

Distinct 5
Distinct (%) 2.8%
Missing 11
Missing (%) 5.8%
Memory size 1.6 KiB
Agree
79
Strongly agree
69
Neither agree or disagree
19
Disagree
8
Strongly disagree
3

Length

Max length 25
Median length 17
Mean length 10.96067416
Min length 5

Characters and Unicode

Total characters 1951
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Strongly agree
2nd row Disagree
3rd row Strongly agree
4th row Strongly agree
5th row Agree

Common Values

Value Count Frequency (%)
Agree 79
41.8%
Strongly agree 69
36.5%
Neither agree or disagree 19
10.1%
Disagree 8
4.2%
Strongly disagree 3
1.6%
(Missing) 11
5.8%

Length

2022-07-04T20:14:18.452374 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:18.727026 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 167
54.4%
strongly 72
23.5%
disagree 30
9.8%
neither 19
6.2%
or 19
6.2%

Most occurring characters

Value Count Frequency (%)
e 432
22.1%
r 307
15.7%
g 269
13.8%
129
6.6%
a 118
6.0%
t 91
4.7%
o 91
4.7%
A 79
4.0%
y 72
3.7%
l 72
3.7%
Other values (8) 291
14.9%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1644
84.3%
Uppercase Letter 178
9.1%
Space Separator 129
6.6%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 432
26.3%
r 307
18.7%
g 269
16.4%
a 118
7.2%
t 91
5.5%
o 91
5.5%
y 72
4.4%
l 72
4.4%
n 72
4.4%
i 49
3.0%
Other values (3) 71
4.3%
Uppercase Letter
Value Count Frequency (%)
A 79
44.4%
S 72
40.4%
N 19
10.7%
D 8
4.5%
Space Separator
Value Count Frequency (%)
129
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1822
93.4%
Common 129
6.6%

Most frequent character per script

Latin
Value Count Frequency (%)
e 432
23.7%
r 307
16.8%
g 269
14.8%
a 118
6.5%
t 91
5.0%
o 91
5.0%
A 79
4.3%
y 72
4.0%
l 72
4.0%
n 72
4.0%
Other values (7) 219
12.0%
Common
Value Count Frequency (%)
129
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1951
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 432
22.1%
r 307
15.7%
g 269
13.8%
129
6.6%
a 118
6.0%
t 91
4.7%
o 91
4.7%
A 79
4.0%
y 72
3.7%
l 72
3.7%
Other values (8) 291
14.9%

UX01[2]
Categorical

HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with the following statements. [It was easy to ask a question in the chatbot]

Distinct 5
Distinct (%) 2.8%
Missing 11
Missing (%) 5.8%
Memory size 1.6 KiB
Agree
83
Strongly agree
52
Neither agree or disagree
25
Disagree
15
Strongly disagree
3

Length

Max length 25
Median length 17
Mean length 10.89325843
Min length 5

Characters and Unicode

Total characters 1939
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Strongly agree
2nd row Neither agree or disagree
3rd row Neither agree or disagree
4th row Agree
5th row Agree

Common Values

Value Count Frequency (%)
Agree 83
43.9%
Strongly agree 52
27.5%
Neither agree or disagree 25
13.2%
Disagree 15
7.9%
Strongly disagree 3
1.6%
(Missing) 11
5.8%

Length

2022-07-04T20:14:18.986736 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:19.260899 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 160
51.9%
strongly 55
17.9%
disagree 43
14.0%
neither 25
8.1%
or 25
8.1%

Most occurring characters

Value Count Frequency (%)
e 456
23.5%
r 308
15.9%
g 258
13.3%
130
6.7%
a 120
6.2%
A 83
4.3%
t 80
4.1%
o 80
4.1%
i 68
3.5%
y 55
2.8%
Other values (8) 301
15.5%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1631
84.1%
Uppercase Letter 178
9.2%
Space Separator 130
6.7%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 456
28.0%
r 308
18.9%
g 258
15.8%
a 120
7.4%
t 80
4.9%
o 80
4.9%
i 68
4.2%
y 55
3.4%
l 55
3.4%
n 55
3.4%
Other values (3) 96
5.9%
Uppercase Letter
Value Count Frequency (%)
A 83
46.6%
S 55
30.9%
N 25
14.0%
D 15
8.4%
Space Separator
Value Count Frequency (%)
130
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1809
93.3%
Common 130
6.7%

Most frequent character per script

Latin
Value Count Frequency (%)
e 456
25.2%
r 308
17.0%
g 258
14.3%
a 120
6.6%
A 83
4.6%
t 80
4.4%
o 80
4.4%
i 68
3.8%
y 55
3.0%
l 55
3.0%
Other values (7) 246
13.6%
Common
Value Count Frequency (%)
130
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1939
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 456
23.5%
r 308
15.9%
g 258
13.3%
130
6.7%
a 120
6.2%
A 83
4.3%
t 80
4.1%
o 80
4.1%
i 68
3.5%
y 55
2.8%
Other values (8) 301
15.5%

UX01[3]
Categorical

HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with the following statements. [It was easy to provide an answer in the chatbot]

Distinct 5
Distinct (%) 2.8%
Missing 11
Missing (%) 5.8%
Memory size 1.6 KiB
Agree
82
Strongly agree
47
Disagree
24
Neither agree or disagree
23
Strongly disagree
2

Length

Max length 25
Median length 17
Mean length 10.5
Min length 5

Characters and Unicode

Total characters 1869
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Strongly agree
2nd row Agree
3rd row Strongly agree
4th row Disagree
5th row Agree

Common Values

Value Count Frequency (%)
Agree 82
43.4%
Strongly agree 47
24.9%
Disagree 24
12.7%
Neither agree or disagree 23
12.2%
Strongly disagree 2
1.1%
(Missing) 11
5.8%

Length

2022-07-04T20:14:19.530827 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:19.815641 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 152
51.4%
strongly 49
16.6%
disagree 49
16.6%
neither 23
7.8%
or 23
7.8%

Most occurring characters

Value Count Frequency (%)
e 448
24.0%
r 296
15.8%
g 250
13.4%
a 119
6.4%
118
6.3%
A 82
4.4%
t 72
3.9%
o 72
3.9%
i 72
3.9%
s 49
2.6%
Other values (8) 291
15.6%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1573
84.2%
Uppercase Letter 178
9.5%
Space Separator 118
6.3%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 448
28.5%
r 296
18.8%
g 250
15.9%
a 119
7.6%
t 72
4.6%
o 72
4.6%
i 72
4.6%
s 49
3.1%
y 49
3.1%
l 49
3.1%
Other values (3) 97
6.2%
Uppercase Letter
Value Count Frequency (%)
A 82
46.1%
S 49
27.5%
D 24
13.5%
N 23
12.9%
Space Separator
Value Count Frequency (%)
118
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1751
93.7%
Common 118
6.3%

Most frequent character per script

Latin
Value Count Frequency (%)
e 448
25.6%
r 296
16.9%
g 250
14.3%
a 119
6.8%
A 82
4.7%
t 72
4.1%
o 72
4.1%
i 72
4.1%
s 49
2.8%
y 49
2.8%
Other values (7) 242
13.8%
Common
Value Count Frequency (%)
118
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1869
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 448
24.0%
r 296
15.8%
g 250
13.4%
a 119
6.4%
118
6.3%
A 82
4.4%
t 72
3.9%
o 72
3.9%
i 72
3.9%
s 49
2.6%
Other values (8) 291
15.6%

UX01[4]
Categorical

HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with the following statements. [It was easy to decide if I liked an answer]

Distinct 5
Distinct (%) 2.8%
Missing 11
Missing (%) 5.8%
Memory size 1.6 KiB
Agree
71
Strongly agree
50
Neither agree or disagree
39
Disagree
15
Strongly disagree
3

Length

Max length 25
Median length 17
Mean length 12.36516854
Min length 5

Characters and Unicode

Total characters 2201
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Strongly agree
2nd row Strongly agree
3rd row Strongly agree
4th row Agree
5th row Agree

Common Values

Value Count Frequency (%)
Agree 71
37.6%
Strongly agree 50
26.5%
Neither agree or disagree 39
20.6%
Disagree 15
7.9%
Strongly disagree 3
1.6%
(Missing) 11
5.8%

Length

2022-07-04T20:14:20.084143 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:20.365544 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 160
46.0%
disagree 57
16.4%
strongly 53
15.2%
neither 39
11.2%
or 39
11.2%

Most occurring characters

Value Count Frequency (%)
e 512
23.3%
r 348
15.8%
g 270
12.3%
170
7.7%
a 146
6.6%
i 96
4.4%
t 92
4.2%
o 92
4.2%
A 71
3.2%
s 57
2.6%
Other values (8) 347
15.8%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1853
84.2%
Uppercase Letter 178
8.1%
Space Separator 170
7.7%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 512
27.6%
r 348
18.8%
g 270
14.6%
a 146
7.9%
i 96
5.2%
t 92
5.0%
o 92
5.0%
s 57
3.1%
y 53
2.9%
l 53
2.9%
Other values (3) 134
7.2%
Uppercase Letter
Value Count Frequency (%)
A 71
39.9%
S 53
29.8%
N 39
21.9%
D 15
8.4%
Space Separator
Value Count Frequency (%)
170
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 2031
92.3%
Common 170
7.7%

Most frequent character per script

Latin
Value Count Frequency (%)
e 512
25.2%
r 348
17.1%
g 270
13.3%
a 146
7.2%
i 96
4.7%
t 92
4.5%
o 92
4.5%
A 71
3.5%
s 57
2.8%
y 53
2.6%
Other values (7) 294
14.5%
Common
Value Count Frequency (%)
170
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 2201
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 512
23.3%
r 348
15.8%
g 270
12.3%
170
7.7%
a 146
6.6%
i 96
4.4%
t 92
4.2%
o 92
4.2%
A 71
3.2%
s 57
2.6%
Other values (8) 347
15.8%

UX01[5]
Categorical

HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with the following statements. [I had the necessary resources to use the chatbot]

Distinct 5
Distinct (%) 2.8%
Missing 11
Missing (%) 5.8%
Memory size 1.6 KiB
Agree
83
Strongly agree
66
Neither agree or disagree
17
Disagree
8
Strongly disagree
4

Length

Max length 25
Median length 17
Mean length 10.65168539
Min length 5

Characters and Unicode

Total characters 1896
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Agree
2nd row Strongly agree
3rd row Strongly agree
4th row Strongly agree
5th row Agree

Common Values

Value Count Frequency (%)
Agree 83
43.9%
Strongly agree 66
34.9%
Neither agree or disagree 17
9.0%
Disagree 8
4.2%
Strongly disagree 4
2.1%
(Missing) 11
5.8%

Length

2022-07-04T20:14:20.625594 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:20.917206 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 166
55.5%
strongly 70
23.4%
disagree 29
9.7%
neither 17
5.7%
or 17
5.7%

Most occurring characters

Value Count Frequency (%)
e 424
22.4%
r 299
15.8%
g 265
14.0%
121
6.4%
a 112
5.9%
t 87
4.6%
o 87
4.6%
A 83
4.4%
y 70
3.7%
l 70
3.7%
Other values (8) 278
14.7%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1597
84.2%
Uppercase Letter 178
9.4%
Space Separator 121
6.4%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 424
26.5%
r 299
18.7%
g 265
16.6%
a 112
7.0%
t 87
5.4%
o 87
5.4%
y 70
4.4%
l 70
4.4%
n 70
4.4%
i 46
2.9%
Other values (3) 67
4.2%
Uppercase Letter
Value Count Frequency (%)
A 83
46.6%
S 70
39.3%
N 17
9.6%
D 8
4.5%
Space Separator
Value Count Frequency (%)
121
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1775
93.6%
Common 121
6.4%

Most frequent character per script

Latin
Value Count Frequency (%)
e 424
23.9%
r 299
16.8%
g 265
14.9%
a 112
6.3%
t 87
4.9%
o 87
4.9%
A 83
4.7%
y 70
3.9%
l 70
3.9%
n 70
3.9%
Other values (7) 208
11.7%
Common
Value Count Frequency (%)
121
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1896
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 424
22.4%
r 299
15.8%
g 265
14.0%
121
6.4%
a 112
5.9%
t 87
4.6%
o 87
4.6%
A 83
4.4%
y 70
3.7%
l 70
3.7%
Other values (8) 278
14.7%

UX01[6]
Categorical

HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with the following statements. [I had the necessary knowledge to use the chatbot]

Distinct 5
Distinct (%) 2.8%
Missing 11
Missing (%) 5.8%
Memory size 1.6 KiB
Agree
74
Strongly agree
62
Neither agree or disagree
22
Disagree
15
Strongly disagree
5

Length

Max length 25
Median length 17
Mean length 11.19662921
Min length 5

Characters and Unicode

Total characters 1993
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Neither agree or disagree
2nd row Agree
3rd row Strongly agree
4th row Disagree
5th row Agree

Common Values

Value Count Frequency (%)
Agree 74
39.2%
Strongly agree 62
32.8%
Neither agree or disagree 22
11.6%
Disagree 15
7.9%
Strongly disagree 5
2.6%
(Missing) 11
5.8%

Length

2022-07-04T20:14:21.178581 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:21.459373 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 158
50.8%
strongly 67
21.5%
disagree 42
13.5%
neither 22
7.1%
or 22
7.1%

Most occurring characters

Value Count Frequency (%)
e 444
22.3%
r 311
15.6%
g 267
13.4%
133
6.7%
a 126
6.3%
t 89
4.5%
o 89
4.5%
A 74
3.7%
y 67
3.4%
l 67
3.4%
Other values (8) 326
16.4%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1682
84.4%
Uppercase Letter 178
8.9%
Space Separator 133
6.7%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 444
26.4%
r 311
18.5%
g 267
15.9%
a 126
7.5%
t 89
5.3%
o 89
5.3%
y 67
4.0%
l 67
4.0%
n 67
4.0%
i 64
3.8%
Other values (3) 91
5.4%
Uppercase Letter
Value Count Frequency (%)
A 74
41.6%
S 67
37.6%
N 22
12.4%
D 15
8.4%
Space Separator
Value Count Frequency (%)
133
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1860
93.3%
Common 133
6.7%

Most frequent character per script

Latin
Value Count Frequency (%)
e 444
23.9%
r 311
16.7%
g 267
14.4%
a 126
6.8%
t 89
4.8%
o 89
4.8%
A 74
4.0%
y 67
3.6%
l 67
3.6%
n 67
3.6%
Other values (7) 259
13.9%
Common
Value Count Frequency (%)
133
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1993
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 444
22.3%
r 311
15.6%
g 267
13.4%
133
6.7%
a 126
6.3%
t 89
4.5%
o 89
4.5%
A 74
3.7%
y 67
3.4%
l 67
3.4%
Other values (8) 326
16.4%

L01[1]
Categorical

HIGH CORRELATION
MISSING

Please select from the options below the one place where you would most likely be on Tuesdays during term time/the semester, at these times of the day. [Before 10:00]

Distinct 6
Distinct (%) 10.9%
Missing 134
Missing (%) 70.9%
Memory size 1.6 KiB
Home
41
University campus
8
Public Indoor space other than university (e.g. library)
2
Work (other than university)
2
Public outdoor place other than university (e.g. park)
1

Length

Max length 56
Median length 4
Mean length 9.654545455
Min length 4

Characters and Unicode

Total characters 531
Distinct characters 30
Distinct categories 6 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 2 ?
Unique (%) 3.6%

Sample

1st row Home
2nd row University campus
3rd row Home
4th row Public Indoor space other than university (e.g. library)
5th row Home

Common Values

Value Count Frequency (%)
Home 41
21.7%
University campus 8
4.2%
Public Indoor space other than university (e.g. library) 2
1.1%
Work (other than university) 2
1.1%
Public outdoor place other than university (e.g. park) 1
0.5%
On the go 1
0.5%
(Missing) 134
70.9%

Length

2022-07-04T20:14:21.715569 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:21.977065 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
home 41
44.6%
university 13
14.1%
campus 8
8.7%
other 5
5.4%
than 5
5.4%
e.g 3
3.3%
public 3
3.3%
space 2
2.2%
indoor 2
2.2%
library 2
2.2%
Other values (7) 8
8.7%

Most occurring characters

Value Count Frequency (%)
e 66
12.4%
o 56
10.5%
m 49
9.2%
H 41
7.7%
37
7.0%
i 31
5.8%
r 28
5.3%
t 25
4.7%
s 23
4.3%
n 21
4.0%
Other values (20) 154
29.0%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 421
79.3%
Uppercase Letter 57
10.7%
Space Separator 37
7.0%
Other Punctuation 6
1.1%
Open Punctuation 5
0.9%
Close Punctuation 5
0.9%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 66
15.7%
o 56
13.3%
m 49
11.6%
i 31
7.4%
r 28
6.7%
t 25
5.9%
s 23
5.5%
n 21
5.0%
a 19
4.5%
u 17
4.0%
Other values (10) 86
20.4%
Uppercase Letter
Value Count Frequency (%)
H 41
71.9%
U 8
14.0%
P 3
5.3%
I 2
3.5%
W 2
3.5%
O 1
1.8%
Space Separator
Value Count Frequency (%)
37
100.0%
Other Punctuation
Value Count Frequency (%)
. 6
100.0%
Open Punctuation
Value Count Frequency (%)
( 5
100.0%
Close Punctuation
Value Count Frequency (%)
) 5
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 478
90.0%
Common 53
10.0%

Most frequent character per script

Latin
Value Count Frequency (%)
e 66
13.8%
o 56
11.7%
m 49
10.3%
H 41
8.6%
i 31
6.5%
r 28
5.9%
t 25
5.2%
s 23
4.8%
n 21
4.4%
a 19
4.0%
Other values (16) 119
24.9%
Common
Value Count Frequency (%)
37
69.8%
. 6
11.3%
( 5
9.4%
) 5
9.4%

Most occurring blocks

Value Count Frequency (%)
ASCII 531
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 66
12.4%
o 56
10.5%
m 49
9.2%
H 41
7.7%
37
7.0%
i 31
5.8%
r 28
5.3%
t 25
4.7%
s 23
4.3%
n 21
4.0%
Other values (20) 154
29.0%

L01[2]
Categorical

HIGH CORRELATION
MISSING

Please select from the options below the one place where you would most likely be on Tuesdays during term time/the semester, at these times of the day. [10:00-12:00]

Distinct 6
Distinct (%) 10.9%
Missing 134
Missing (%) 70.9%
Memory size 1.6 KiB
Home
31
University campus
14
Work (other than university)
4
On the go
3
Public Indoor space other than university (e.g. library)
2

Length

Max length 56
Median length 4
Mean length 12.12727273
Min length 4

Characters and Unicode

Total characters 667
Distinct characters 30
Distinct categories 6 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 1 ?
Unique (%) 1.8%

Sample

1st row Home
2nd row Home
3rd row University campus
4th row Public Indoor space other than university (e.g. library)
5th row Work (other than university)

Common Values

Value Count Frequency (%)
Home 31
16.4%
University campus 14
7.4%
Work (other than university) 4
2.1%
On the go 3
1.6%
Public Indoor space other than university (e.g. library) 2
1.1%
Public outdoor place other than university (e.g. park) 1
0.5%
(Missing) 134
70.9%

Length

2022-07-04T20:14:22.243909 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:22.493889 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
home 31
28.7%
university 21
19.4%
campus 14
13.0%
other 7
6.5%
than 7
6.5%
work 4
3.7%
e.g 3
2.8%
public 3
2.8%
go 3
2.8%
the 3
2.8%
Other values (7) 12
11.1%

Most occurring characters

Value Count Frequency (%)
e 68
10.2%
53
7.9%
o 52
7.8%
i 47
7.0%
m 45
6.7%
r 40
6.0%
t 39
5.8%
s 37
5.5%
n 33
4.9%
H 31
4.6%
Other values (20) 222
33.3%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 537
80.5%
Uppercase Letter 57
8.5%
Space Separator 53
7.9%
Open Punctuation 7
1.0%
Close Punctuation 7
1.0%
Other Punctuation 6
0.9%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 68
12.7%
o 52
9.7%
i 47
8.8%
m 45
8.4%
r 40
7.4%
t 39
7.3%
s 37
6.9%
n 33
6.1%
a 27
5.0%
u 25
4.7%
Other values (10) 124
23.1%
Uppercase Letter
Value Count Frequency (%)
H 31
54.4%
U 14
24.6%
W 4
7.0%
O 3
5.3%
P 3
5.3%
I 2
3.5%
Space Separator
Value Count Frequency (%)
53
100.0%
Open Punctuation
Value Count Frequency (%)
( 7
100.0%
Close Punctuation
Value Count Frequency (%)
) 7
100.0%
Other Punctuation
Value Count Frequency (%)
. 6
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 594
89.1%
Common 73
10.9%

Most frequent character per script

Latin
Value Count Frequency (%)
e 68
11.4%
o 52
8.8%
i 47
7.9%
m 45
7.6%
r 40
6.7%
t 39
6.6%
s 37
6.2%
n 33
5.6%
H 31
5.2%
a 27
4.5%
Other values (16) 175
29.5%
Common
Value Count Frequency (%)
53
72.6%
( 7
9.6%
) 7
9.6%
. 6
8.2%

Most occurring blocks

Value Count Frequency (%)
ASCII 667
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 68
10.2%
53
7.9%
o 52
7.8%
i 47
7.0%
m 45
6.7%
r 40
6.0%
t 39
5.8%
s 37
5.5%
n 33
4.9%
H 31
4.6%
Other values (20) 222
33.3%

L01[3]
Categorical

HIGH CORRELATION
MISSING

Please select from the options below the one place where you would most likely be on Tuesdays during term time/the semester, at these times of the day. [12:00-14:00]

Distinct 7
Distinct (%) 12.7%
Missing 134
Missing (%) 70.9%
Memory size 1.6 KiB
Home
30
University campus
14
Work (other than university)
3
Restaurant/cafe (including university cafe/restaurant)
3
On the go
2
Other values (2)
3

Length

Max length 56
Median length 4
Mean length 14.29090909
Min length 4

Characters and Unicode

Total characters 786
Distinct characters 33
Distinct categories 6 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 1 ?
Unique (%) 1.8%

Sample

1st row Home
2nd row Home
3rd row Home
4th row Home
5th row Work (other than university)

Common Values

Value Count Frequency (%)
Home 30
15.9%
University campus 14
7.4%
Work (other than university) 3
1.6%
Restaurant/cafe (including university cafe/restaurant) 3
1.6%
On the go 2
1.1%
Public outdoor place other than university (e.g. park) 2
1.1%
Public Indoor space other than university (e.g. library) 1
0.5%
(Missing) 134
70.9%

Length

2022-07-04T20:14:22.754540 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:23.011151 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
home 30
26.8%
university 23
20.5%
campus 14
12.5%
other 6
5.4%
than 6
5.4%
work 3
2.7%
restaurant/cafe 3
2.7%
including 3
2.7%
cafe/restaurant 3
2.7%
e.g 3
2.7%
Other values (10) 18
16.1%

Most occurring characters

Value Count Frequency (%)
e 79
10.1%
57
7.3%
i 56
7.1%
t 51
6.5%
o 49
6.2%
r 48
6.1%
m 44
5.6%
n 44
5.6%
a 44
5.6%
s 44
5.6%
Other values (23) 270
34.4%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 643
81.8%
Space Separator 57
7.3%
Uppercase Letter 56
7.1%
Other Punctuation 12
1.5%
Open Punctuation 9
1.1%
Close Punctuation 9
1.1%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 79
12.3%
i 56
8.7%
t 51
7.9%
o 49
7.6%
r 48
7.5%
m 44
6.8%
n 44
6.8%
a 44
6.8%
s 44
6.8%
u 37
5.8%
Other values (11) 147
22.9%
Uppercase Letter
Value Count Frequency (%)
H 30
53.6%
U 14
25.0%
P 3
5.4%
R 3
5.4%
W 3
5.4%
O 2
3.6%
I 1
1.8%
Other Punctuation
Value Count Frequency (%)
. 6
50.0%
/ 6
50.0%
Space Separator
Value Count Frequency (%)
57
100.0%
Open Punctuation
Value Count Frequency (%)
( 9
100.0%
Close Punctuation
Value Count Frequency (%)
) 9
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 699
88.9%
Common 87
11.1%

Most frequent character per script

Latin
Value Count Frequency (%)
e 79
11.3%
i 56
8.0%
t 51
7.3%
o 49
7.0%
r 48
6.9%
m 44
6.3%
n 44
6.3%
a 44
6.3%
s 44
6.3%
u 37
5.3%
Other values (18) 203
29.0%
Common
Value Count Frequency (%)
57
65.5%
( 9
10.3%
) 9
10.3%
. 6
6.9%
/ 6
6.9%

Most occurring blocks

Value Count Frequency (%)
ASCII 786
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 79
10.1%
57
7.3%
i 56
7.1%
t 51
6.5%
o 49
6.2%
r 48
6.1%
m 44
5.6%
n 44
5.6%
a 44
5.6%
s 44
5.6%
Other values (23) 270
34.4%

L01[4]
Categorical

HIGH CORRELATION
MISSING

Please select from the options below the one place where you would most likely be on Tuesdays during term time/the semester, at these times of the day. [14:00-16:00]

Distinct 7
Distinct (%) 12.7%
Missing 134
Missing (%) 70.9%
Memory size 1.6 KiB
Home
30
University campus
12
Work (other than university)
5
Public Indoor space other than university (e.g. library)
3
Public outdoor place other than university (e.g. park)
2
Other values (2)
3

Length

Max length 56
Median length 4
Mean length 14.12727273
Min length 4

Characters and Unicode

Total characters 777
Distinct characters 32
Distinct categories 6 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 1 ?
Unique (%) 1.8%

Sample

1st row Public outdoor place other than university (e.g. park)
2nd row Home
3rd row Home
4th row Home
5th row Work (other than university)

Common Values

Value Count Frequency (%)
Home 30
15.9%
University campus 12
6.3%
Work (other than university) 5
2.6%
Public Indoor space other than university (e.g. library) 3
1.6%
Public outdoor place other than university (e.g. park) 2
1.1%
On the go 2
1.1%
Someone else's home 1
0.5%
(Missing) 134
70.9%

Length

2022-07-04T20:14:23.287951 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:23.544209 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
home 31
25.2%
university 22
17.9%
campus 12
9.8%
other 10
8.1%
than 10
8.1%
public 5
4.1%
e.g 5
4.1%
work 5
4.1%
indoor 3
2.4%
space 3
2.4%
Other values (9) 17
13.8%

Most occurring characters

Value Count Frequency (%)
e 79
10.2%
68
8.8%
o 62
8.0%
i 52
6.7%
r 50
6.4%
t 46
5.9%
m 44
5.7%
s 39
5.0%
n 38
4.9%
a 32
4.1%
Other values (22) 267
34.4%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 620
79.8%
Space Separator 68
8.8%
Uppercase Letter 58
7.5%
Other Punctuation 11
1.4%
Open Punctuation 10
1.3%
Close Punctuation 10
1.3%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 79
12.7%
o 62
10.0%
i 52
8.4%
r 50
8.1%
t 46
7.4%
m 44
7.1%
s 39
6.3%
n 38
6.1%
a 32
5.2%
u 29
4.7%
Other values (10) 149
24.0%
Uppercase Letter
Value Count Frequency (%)
H 30
51.7%
U 12
20.7%
P 5
8.6%
W 5
8.6%
I 3
5.2%
O 2
3.4%
S 1
1.7%
Other Punctuation
Value Count Frequency (%)
. 10
90.9%
' 1
9.1%
Space Separator
Value Count Frequency (%)
68
100.0%
Open Punctuation
Value Count Frequency (%)
( 10
100.0%
Close Punctuation
Value Count Frequency (%)
) 10
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 678
87.3%
Common 99
12.7%

Most frequent character per script

Latin
Value Count Frequency (%)
e 79
11.7%
o 62
9.1%
i 52
7.7%
r 50
7.4%
t 46
6.8%
m 44
6.5%
s 39
5.8%
n 38
5.6%
a 32
4.7%
H 30
4.4%
Other values (17) 206
30.4%
Common
Value Count Frequency (%)
68
68.7%
( 10
10.1%
) 10
10.1%
. 10
10.1%
' 1
1.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 777
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 79
10.2%
68
8.8%
o 62
8.0%
i 52
6.7%
r 50
6.4%
t 46
5.9%
m 44
5.7%
s 39
5.0%
n 38
4.9%
a 32
4.1%
Other values (22) 267
34.4%

L01[5]
Categorical

HIGH CORRELATION
MISSING

Please select from the options below the one place where you would most likely be on Tuesdays during term time/the semester, at these times of the day. [16:00-18:00]

Distinct 8
Distinct (%) 14.5%
Missing 134
Missing (%) 70.9%
Memory size 1.6 KiB
Home
36
University campus
7
Public outdoor place other than university (e.g. park)
4
Work (other than university)
3
Public Indoor space other than university (e.g. library)
2
Other values (3)
3

Length

Max length 56
Median length 4
Mean length 13.76363636
Min length 4

Characters and Unicode

Total characters 757
Distinct characters 35
Distinct categories 6 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 3 ?
Unique (%) 5.5%

Sample

1st row Public outdoor place other than university (e.g. park)
2nd row Home
3rd row Home
4th row Home
5th row Work (other than university)

Common Values

Value Count Frequency (%)
Home 36
19.0%
University campus 7
3.7%
Public outdoor place other than university (e.g. park) 4
2.1%
Work (other than university) 3
1.6%
Public Indoor space other than university (e.g. library) 2
1.1%
On the go 1
0.5%
Someone else's home 1
0.5%
Restaurant/cafe (including university cafe/restaurant) 1
0.5%
(Missing) 134
70.9%

Length

2022-07-04T20:14:23.828562 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:24.092117 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
home 37
30.8%
university 17
14.2%
other 9
7.5%
than 9
7.5%
campus 7
5.8%
public 6
5.0%
e.g 6
5.0%
outdoor 4
3.3%
place 4
3.3%
park 4
3.3%
Other values (12) 17
14.2%

Most occurring characters

Value Count Frequency (%)
e 84
11.1%
o 68
9.0%
65
8.6%
r 46
6.1%
m 45
5.9%
i 44
5.8%
t 44
5.8%
H 36
4.8%
n 34
4.5%
a 34
4.5%
Other values (25) 257
33.9%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 600
79.3%
Space Separator 65
8.6%
Uppercase Letter 57
7.5%
Other Punctuation 15
2.0%
Close Punctuation 10
1.3%
Open Punctuation 10
1.3%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 84
14.0%
o 68
11.3%
r 46
7.7%
m 45
7.5%
i 44
7.3%
t 44
7.3%
n 34
5.7%
a 34
5.7%
u 30
5.0%
s 30
5.0%
Other values (11) 141
23.5%
Uppercase Letter
Value Count Frequency (%)
H 36
63.2%
U 7
12.3%
P 6
10.5%
W 3
5.3%
I 2
3.5%
O 1
1.8%
S 1
1.8%
R 1
1.8%
Other Punctuation
Value Count Frequency (%)
. 12
80.0%
/ 2
13.3%
' 1
6.7%
Space Separator
Value Count Frequency (%)
65
100.0%
Close Punctuation
Value Count Frequency (%)
) 10
100.0%
Open Punctuation
Value Count Frequency (%)
( 10
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 657
86.8%
Common 100
13.2%

Most frequent character per script

Latin
Value Count Frequency (%)
e 84
12.8%
o 68
10.4%
r 46
7.0%
m 45
6.8%
i 44
6.7%
t 44
6.7%
H 36
5.5%
n 34
5.2%
a 34
5.2%
u 30
4.6%
Other values (19) 192
29.2%
Common
Value Count Frequency (%)
65
65.0%
. 12
12.0%
) 10
10.0%
( 10
10.0%
/ 2
2.0%
' 1
1.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 757
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 84
11.1%
o 68
9.0%
65
8.6%
r 46
6.1%
m 45
5.9%
i 44
5.8%
t 44
5.8%
H 36
4.8%
n 34
4.5%
a 34
4.5%
Other values (25) 257
33.9%

L01[6]
Categorical

HIGH CORRELATION
MISSING

Please select from the options below the one place where you would most likely be on Tuesdays during term time/the semester, at these times of the day. [After 18:00]

Distinct 7
Distinct (%) 12.7%
Missing 134
Missing (%) 70.9%
Memory size 1.6 KiB
Home
42
On the go
6
University campus
2
Public outdoor place other than university (e.g. park)
2
Work (other than university)
1
Other values (2)
2

Length

Max length 54
Median length 4
Mean length 8.454545455
Min length 4

Characters and Unicode

Total characters 465
Distinct characters 34
Distinct categories 6 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 3 ?
Unique (%) 5.5%

Sample

1st row Home
2nd row Home
3rd row Home
4th row On the go
5th row On the go

Common Values

Value Count Frequency (%)
Home 42
22.2%
On the go 6
3.2%
University campus 2
1.1%
Public outdoor place other than university (e.g. park) 2
1.1%
Work (other than university) 1
0.5%
Someone else's home 1
0.5%
Restaurant/cafe (including university cafe/restaurant) 1
0.5%
(Missing) 134
70.9%

Length

2022-07-04T20:14:24.392676 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:24.640779 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
home 43
47.3%
on 6
6.6%
the 6
6.6%
go 6
6.6%
university 6
6.6%
than 3
3.3%
other 3
3.3%
outdoor 2
2.2%
place 2
2.2%
public 2
2.2%
Other values (9) 12
13.2%

Most occurring characters

Value Count Frequency (%)
e 70
15.1%
o 61
13.1%
m 46
9.9%
H 42
9.0%
36
7.7%
t 24
5.2%
n 20
4.3%
r 17
3.7%
i 16
3.4%
a 15
3.2%
Other values (24) 118
25.4%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 359
77.2%
Uppercase Letter 55
11.8%
Space Separator 36
7.7%
Other Punctuation 7
1.5%
Close Punctuation 4
0.9%
Open Punctuation 4
0.9%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 70
19.5%
o 61
17.0%
m 46
12.8%
t 24
6.7%
n 20
5.6%
r 17
4.7%
i 16
4.5%
a 15
4.2%
h 13
3.6%
u 13
3.6%
Other values (11) 64
17.8%
Uppercase Letter
Value Count Frequency (%)
H 42
76.4%
O 6
10.9%
U 2
3.6%
P 2
3.6%
W 1
1.8%
S 1
1.8%
R 1
1.8%
Other Punctuation
Value Count Frequency (%)
. 4
57.1%
/ 2
28.6%
' 1
14.3%
Space Separator
Value Count Frequency (%)
36
100.0%
Close Punctuation
Value Count Frequency (%)
) 4
100.0%
Open Punctuation
Value Count Frequency (%)
( 4
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 414
89.0%
Common 51
11.0%

Most frequent character per script

Latin
Value Count Frequency (%)
e 70
16.9%
o 61
14.7%
m 46
11.1%
H 42
10.1%
t 24
5.8%
n 20
4.8%
r 17
4.1%
i 16
3.9%
a 15
3.6%
h 13
3.1%
Other values (18) 90
21.7%
Common
Value Count Frequency (%)
36
70.6%
) 4
7.8%
( 4
7.8%
. 4
7.8%
/ 2
3.9%
' 1
2.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 465
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 70
15.1%
o 61
13.1%
m 46
9.9%
H 42
9.0%
36
7.7%
t 24
5.2%
n 20
4.3%
r 17
3.7%
i 16
3.4%
a 15
3.2%
Other values (24) 118
25.4%

L02[1]
Categorical

HIGH CORRELATION
MISSING

Please select from the options below the one place where you would most likely be on Saturdays during term time/the semester, at these times of the day. [Before 10:00]

Distinct 5
Distinct (%) 9.1%
Missing 134
Missing (%) 70.9%
Memory size 1.6 KiB
Home
48
Work (other than university)
2
Public outdoor place other than university (e.g. park)
2
On the go
2
Someone else's home
1

Length

Max length 54
Median length 4
Mean length 7.145454545
Min length 4

Characters and Unicode

Total characters 393
Distinct characters 30
Distinct categories 6 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 1 ?
Unique (%) 1.8%

Sample

1st row Home
2nd row Home
3rd row Home
4th row Home
5th row Work (other than university)

Common Values

Value Count Frequency (%)
Home 48
25.4%
Work (other than university) 2
1.1%
Public outdoor place other than university (e.g. park) 2
1.1%
On the go 2
1.1%
Someone else's home 1
0.5%
(Missing) 134
70.9%

Length

2022-07-04T20:14:24.921023 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:25.721409 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
home 49
60.5%
other 4
4.9%
than 4
4.9%
university 4
4.9%
work 2
2.5%
public 2
2.5%
outdoor 2
2.5%
place 2
2.5%
e.g 2
2.5%
park 2
2.5%
Other values (5) 8
9.9%

Most occurring characters

Value Count Frequency (%)
e 67
17.0%
o 65
16.5%
m 50
12.7%
H 48
12.2%
26
6.6%
t 16
4.1%
r 14
3.6%
h 11
2.8%
n 11
2.8%
i 10
2.5%
Other values (20) 75
19.1%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 299
76.1%
Uppercase Letter 55
14.0%
Space Separator 26
6.6%
Other Punctuation 5
1.3%
Close Punctuation 4
1.0%
Open Punctuation 4
1.0%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 67
22.4%
o 65
21.7%
m 50
16.7%
t 16
5.4%
r 14
4.7%
h 11
3.7%
n 11
3.7%
i 10
3.3%
a 8
2.7%
u 8
2.7%
Other values (10) 39
13.0%
Uppercase Letter
Value Count Frequency (%)
H 48
87.3%
P 2
3.6%
W 2
3.6%
O 2
3.6%
S 1
1.8%
Other Punctuation
Value Count Frequency (%)
. 4
80.0%
' 1
20.0%
Space Separator
Value Count Frequency (%)
26
100.0%
Close Punctuation
Value Count Frequency (%)
) 4
100.0%
Open Punctuation
Value Count Frequency (%)
( 4
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 354
90.1%
Common 39
9.9%

Most frequent character per script

Latin
Value Count Frequency (%)
e 67
18.9%
o 65
18.4%
m 50
14.1%
H 48
13.6%
t 16
4.5%
r 14
4.0%
h 11
3.1%
n 11
3.1%
i 10
2.8%
a 8
2.3%
Other values (15) 54
15.3%
Common
Value Count Frequency (%)
26
66.7%
. 4
10.3%
) 4
10.3%
( 4
10.3%
' 1
2.6%

Most occurring blocks

Value Count Frequency (%)
ASCII 393
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 67
17.0%
o 65
16.5%
m 50
12.7%
H 48
12.2%
26
6.6%
t 16
4.1%
r 14
3.6%
h 11
2.8%
n 11
2.8%
i 10
2.5%
Other values (20) 75
19.1%

L02[2]
Categorical

HIGH CORRELATION
MISSING

Please select from the options below the one place where you would most likely be on Saturdays during term time/the semester, at these times of the day. [10:00-12:00]

Distinct 7
Distinct (%) 12.7%
Missing 134
Missing (%) 70.9%
Memory size 1.6 KiB
Home
40
On the go
4
University campus
3
Public outdoor place other than university (e.g. park)
3
Work (other than university)
2
Other values (2)
3

Length

Max length 54
Median length 4
Mean length 10.12727273
Min length 4

Characters and Unicode

Total characters 557
Distinct characters 34
Distinct categories 6 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 1 ?
Unique (%) 1.8%

Sample

1st row Home
2nd row On the go
3rd row University campus
4th row Home
5th row Work (other than university)

Common Values

Value Count Frequency (%)
Home 40
21.2%
On the go 4
2.1%
University campus 3
1.6%
Public outdoor place other than university (e.g. park) 3
1.6%
Work (other than university) 2
1.1%
Someone else's home 2
1.1%
Restaurant/cafe (including university cafe/restaurant) 1
0.5%
(Missing) 134
70.9%

Length

2022-07-04T20:14:25.950558 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:26.194521 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
home 42
42.0%
university 9
9.0%
than 5
5.0%
other 5
5.0%
go 4
4.0%
on 4
4.0%
the 4
4.0%
campus 3
3.0%
public 3
3.0%
outdoor 3
3.0%
Other values (9) 18
18.0%

Most occurring characters

Value Count Frequency (%)
e 78
14.0%
o 66
11.8%
m 47
8.4%
45
8.1%
H 40
7.2%
t 30
5.4%
r 25
4.5%
n 24
4.3%
i 23
4.1%
a 20
3.6%
Other values (24) 159
28.5%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 435
78.1%
Uppercase Letter 55
9.9%
Space Separator 45
8.1%
Other Punctuation 10
1.8%
Close Punctuation 6
1.1%
Open Punctuation 6
1.1%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 78
17.9%
o 66
15.2%
m 47
10.8%
t 30
6.9%
r 25
5.7%
n 24
5.5%
i 23
5.3%
a 20
4.6%
s 18
4.1%
u 18
4.1%
Other values (11) 86
19.8%
Uppercase Letter
Value Count Frequency (%)
H 40
72.7%
O 4
7.3%
U 3
5.5%
P 3
5.5%
W 2
3.6%
S 2
3.6%
R 1
1.8%
Other Punctuation
Value Count Frequency (%)
. 6
60.0%
' 2
20.0%
/ 2
20.0%
Space Separator
Value Count Frequency (%)
45
100.0%
Close Punctuation
Value Count Frequency (%)
) 6
100.0%
Open Punctuation
Value Count Frequency (%)
( 6
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 490
88.0%
Common 67
12.0%

Most frequent character per script

Latin
Value Count Frequency (%)
e 78
15.9%
o 66
13.5%
m 47
9.6%
H 40
8.2%
t 30
6.1%
r 25
5.1%
n 24
4.9%
i 23
4.7%
a 20
4.1%
s 18
3.7%
Other values (18) 119
24.3%
Common
Value Count Frequency (%)
45
67.2%
. 6
9.0%
) 6
9.0%
( 6
9.0%
' 2
3.0%
/ 2
3.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 557
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 78
14.0%
o 66
11.8%
m 47
8.4%
45
8.1%
H 40
7.2%
t 30
5.4%
r 25
4.5%
n 24
4.3%
i 23
4.1%
a 20
3.6%
Other values (24) 159
28.5%

L02[3]
Categorical

HIGH CORRELATION
MISSING

Please select from the options below the one place where you would most likely be on Saturdays during term time/the semester, at these times of the day. [12:00-14:00]

Distinct 7
Distinct (%) 12.7%
Missing 134
Missing (%) 70.9%
Memory size 1.6 KiB
Home
26
Public outdoor place other than university (e.g. park)
7
On the go
6
Someone else's home
6
University campus
4
Other values (2)
6

Length

Max length 54
Median length 28
Mean length 18
Min length 4

Characters and Unicode

Total characters 990
Distinct characters 34
Distinct categories 6 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Public outdoor place other than university (e.g. park)
2nd row On the go
3rd row University campus
4th row Restaurant/cafe (including university cafe/restaurant)
5th row Home

Common Values

Value Count Frequency (%)
Home 26
13.8%
Public outdoor place other than university (e.g. park) 7
3.7%
On the go 6
3.2%
Someone else's home 6
3.2%
University campus 4
2.1%
Restaurant/cafe (including university cafe/restaurant) 4
2.1%
Work (other than university) 2
1.1%
(Missing) 134
70.9%

Length

2022-07-04T20:14:26.456806 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:26.712029 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
home 32
21.3%
university 17
11.3%
other 9
6.0%
than 9
6.0%
outdoor 7
4.7%
place 7
4.7%
e.g 7
4.7%
park 7
4.7%
public 7
4.7%
someone 6
4.0%
Other values (9) 42
28.0%

Most occurring characters

Value Count Frequency (%)
e 118
11.9%
95
9.6%
o 82
8.3%
t 64
6.5%
n 54
5.5%
r 54
5.5%
a 51
5.2%
i 49
4.9%
u 43
4.3%
m 42
4.2%
Other values (24) 338
34.1%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 786
79.4%
Space Separator 95
9.6%
Uppercase Letter 55
5.6%
Other Punctuation 28
2.8%
Close Punctuation 13
1.3%
Open Punctuation 13
1.3%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 118
15.0%
o 82
10.4%
t 64
8.1%
n 54
6.9%
r 54
6.9%
a 51
6.5%
i 49
6.2%
u 43
5.5%
m 42
5.3%
s 41
5.2%
Other values (11) 188
23.9%
Uppercase Letter
Value Count Frequency (%)
H 26
47.3%
P 7
12.7%
O 6
10.9%
S 6
10.9%
U 4
7.3%
R 4
7.3%
W 2
3.6%
Other Punctuation
Value Count Frequency (%)
. 14
50.0%
/ 8
28.6%
' 6
21.4%
Space Separator
Value Count Frequency (%)
95
100.0%
Close Punctuation
Value Count Frequency (%)
) 13
100.0%
Open Punctuation
Value Count Frequency (%)
( 13
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 841
84.9%
Common 149
15.1%

Most frequent character per script

Latin
Value Count Frequency (%)
e 118
14.0%
o 82
9.8%
t 64
7.6%
n 54
6.4%
r 54
6.4%
a 51
6.1%
i 49
5.8%
u 43
5.1%
m 42
5.0%
s 41
4.9%
Other values (18) 243
28.9%
Common
Value Count Frequency (%)
95
63.8%
. 14
9.4%
) 13
8.7%
( 13
8.7%
/ 8
5.4%
' 6
4.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 990
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 118
11.9%
95
9.6%
o 82
8.3%
t 64
6.5%
n 54
5.5%
r 54
5.5%
a 51
5.2%
i 49
4.9%
u 43
4.3%
m 42
4.2%
Other values (24) 338
34.1%

L02[4]
Categorical

HIGH CORRELATION
MISSING

Please select from the options below the one place where you would most likely be on Saturdays during term time/the semester, at these times of the day. [14:00-16:00]

Distinct 8
Distinct (%) 14.5%
Missing 134
Missing (%) 70.9%
Memory size 1.6 KiB
Home
24
Public outdoor place other than university (e.g. park)
14
On the go
6
University campus
3
Someone else's home
3
Other values (3)
5

Length

Max length 56
Median length 54
Mean length 22.47272727
Min length 4

Characters and Unicode

Total characters 1236
Distinct characters 35
Distinct categories 6 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 1 ?
Unique (%) 1.8%

Sample

1st row Public outdoor place other than university (e.g. park)
2nd row On the go
3rd row University campus
4th row Home
5th row Public outdoor place other than university (e.g. park)

Common Values

Value Count Frequency (%)
Home 24
12.7%
Public outdoor place other than university (e.g. park) 14
7.4%
On the go 6
3.2%
University campus 3
1.6%
Someone else's home 3
1.6%
Work (other than university) 2
1.1%
Public Indoor space other than university (e.g. library) 2
1.1%
Restaurant/cafe (including university cafe/restaurant) 1
0.5%
(Missing) 134
70.9%

Length

2022-07-04T20:14:26.996074 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:27.257145 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
home 27
13.7%
university 22
11.2%
other 18
9.1%
than 18
9.1%
e.g 16
8.1%
public 16
8.1%
outdoor 14
7.1%
place 14
7.1%
park 14
7.1%
the 6
3.0%
Other values (12) 32
16.2%

Most occurring characters

Value Count Frequency (%)
142
11.5%
e 121
9.8%
o 105
8.5%
t 82
6.6%
r 79
6.4%
i 64
5.2%
a 59
4.8%
n 55
4.4%
u 55
4.4%
h 45
3.6%
Other values (25) 429
34.7%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 962
77.8%
Space Separator 142
11.5%
Uppercase Letter 57
4.6%
Other Punctuation 37
3.0%
Open Punctuation 19
1.5%
Close Punctuation 19
1.5%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 121
12.6%
o 105
10.9%
t 82
8.5%
r 79
8.2%
i 64
6.7%
a 59
6.1%
n 55
5.7%
u 55
5.7%
h 45
4.7%
c 38
4.0%
Other values (11) 259
26.9%
Uppercase Letter
Value Count Frequency (%)
H 24
42.1%
P 16
28.1%
O 6
10.5%
U 3
5.3%
S 3
5.3%
W 2
3.5%
I 2
3.5%
R 1
1.8%
Other Punctuation
Value Count Frequency (%)
. 32
86.5%
' 3
8.1%
/ 2
5.4%
Space Separator
Value Count Frequency (%)
142
100.0%
Open Punctuation
Value Count Frequency (%)
( 19
100.0%
Close Punctuation
Value Count Frequency (%)
) 19
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1019
82.4%
Common 217
17.6%

Most frequent character per script

Latin
Value Count Frequency (%)
e 121
11.9%
o 105
10.3%
t 82
8.0%
r 79
7.8%
i 64
6.3%
a 59
5.8%
n 55
5.4%
u 55
5.4%
h 45
4.4%
c 38
3.7%
Other values (19) 316
31.0%
Common
Value Count Frequency (%)
142
65.4%
. 32
14.7%
( 19
8.8%
) 19
8.8%
' 3
1.4%
/ 2
0.9%

Most occurring blocks

Value Count Frequency (%)
ASCII 1236
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
142
11.5%
e 121
9.8%
o 105
8.5%
t 82
6.6%
r 79
6.4%
i 64
5.2%
a 59
4.8%
n 55
4.4%
u 55
4.4%
h 45
3.6%
Other values (25) 429
34.7%

L02[5]
Categorical

HIGH CORRELATION
MISSING

Please select from the options below the one place where you would most likely be on Saturdays during term time/the semester, at these times of the day. [16:00-18:00]

Distinct 7
Distinct (%) 12.7%
Missing 134
Missing (%) 70.9%
Memory size 1.6 KiB
Home
24
Public outdoor place other than university (e.g. park)
12
On the go
7
Someone else's home
4
University campus
3
Other values (2)
5

Length

Max length 56
Median length 54
Mean length 21.05454545
Min length 4

Characters and Unicode

Total characters 1158
Distinct characters 32
Distinct categories 6 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Public outdoor place other than university (e.g. park)
2nd row On the go
3rd row University campus
4th row Home
5th row Public outdoor place other than university (e.g. park)

Common Values

Value Count Frequency (%)
Home 24
12.7%
Public outdoor place other than university (e.g. park) 12
6.3%
On the go 7
3.7%
Someone else's home 4
2.1%
University campus 3
1.6%
Public Indoor space other than university (e.g. library) 3
1.6%
Work (other than university) 2
1.1%
(Missing) 134
70.9%

Length

2022-07-04T20:14:27.560027 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:27.821994 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
home 28
14.7%
university 20
10.5%
other 17
8.9%
than 17
8.9%
e.g 15
7.9%
public 15
7.9%
outdoor 12
6.3%
place 12
6.3%
park 12
6.3%
go 7
3.7%
Other values (9) 36
18.8%

Most occurring characters

Value Count Frequency (%)
136
11.7%
e 118
10.2%
o 104
9.0%
t 73
6.3%
r 72
6.2%
i 58
5.0%
n 51
4.4%
a 50
4.3%
u 47
4.1%
h 45
3.9%
Other values (22) 404
34.9%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 896
77.4%
Space Separator 136
11.7%
Uppercase Letter 58
5.0%
Other Punctuation 34
2.9%
Open Punctuation 17
1.5%
Close Punctuation 17
1.5%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 118
13.2%
o 104
11.6%
t 73
8.1%
r 72
8.0%
i 58
6.5%
n 51
5.7%
a 50
5.6%
u 47
5.2%
h 45
5.0%
m 35
3.9%
Other values (10) 243
27.1%
Uppercase Letter
Value Count Frequency (%)
H 24
41.4%
P 15
25.9%
O 7
12.1%
S 4
6.9%
U 3
5.2%
I 3
5.2%
W 2
3.4%
Other Punctuation
Value Count Frequency (%)
. 30
88.2%
' 4
11.8%
Space Separator
Value Count Frequency (%)
136
100.0%
Open Punctuation
Value Count Frequency (%)
( 17
100.0%
Close Punctuation
Value Count Frequency (%)
) 17
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 954
82.4%
Common 204
17.6%

Most frequent character per script

Latin
Value Count Frequency (%)
e 118
12.4%
o 104
10.9%
t 73
7.7%
r 72
7.5%
i 58
6.1%
n 51
5.3%
a 50
5.2%
u 47
4.9%
h 45
4.7%
m 35
3.7%
Other values (17) 301
31.6%
Common
Value Count Frequency (%)
136
66.7%
. 30
14.7%
( 17
8.3%
) 17
8.3%
' 4
2.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1158
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
136
11.7%
e 118
10.2%
o 104
9.0%
t 73
6.3%
r 72
6.2%
i 58
5.0%
n 51
4.4%
a 50
4.3%
u 47
4.1%
h 45
3.9%
Other values (22) 404
34.9%

L02[6]
Categorical

HIGH CORRELATION
MISSING

Please select from the options below the one place where you would most likely be on Saturdays during term time/the semester, at these times of the day. [After 18:00]

Distinct 7
Distinct (%) 12.7%
Missing 134
Missing (%) 70.9%
Memory size 1.6 KiB
Home
31
On the go
9
Someone else's home
5
Restaurant/cafe (including university cafe/restaurant)
4
Public outdoor place other than university (e.g. park)
3
Other values (2)
3

Length

Max length 56
Median length 4
Mean length 13.96363636
Min length 4

Characters and Unicode

Total characters 768
Distinct characters 34
Distinct categories 6 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 1 ?
Unique (%) 1.8%

Sample

1st row Restaurant/cafe (including university cafe/restaurant)
2nd row On the go
3rd row University campus
4th row On the go
5th row Home

Common Values

Value Count Frequency (%)
Home 31
16.4%
On the go 9
4.8%
Someone else's home 5
2.6%
Restaurant/cafe (including university cafe/restaurant) 4
2.1%
Public outdoor place other than university (e.g. park) 3
1.6%
University campus 2
1.1%
Public Indoor space other than university (e.g. library) 1
0.5%
(Missing) 134
70.9%

Length

2022-07-04T20:14:28.110038 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:28.384664 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
home 36
28.8%
university 10
8.0%
on 9
7.2%
the 9
7.2%
go 9
7.2%
someone 5
4.0%
else's 5
4.0%
e.g 4
3.2%
than 4
3.2%
other 4
3.2%
Other values (11) 30
24.0%

Most occurring characters

Value Count Frequency (%)
e 103
13.4%
70
9.1%
o 70
9.1%
t 46
6.0%
n 45
5.9%
m 43
5.6%
a 38
4.9%
r 35
4.6%
i 33
4.3%
s 31
4.0%
Other values (24) 254
33.1%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 605
78.8%
Space Separator 70
9.1%
Uppercase Letter 56
7.3%
Other Punctuation 21
2.7%
Open Punctuation 8
1.0%
Close Punctuation 8
1.0%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 103
17.0%
o 70
11.6%
t 46
7.6%
n 45
7.4%
m 43
7.1%
a 38
6.3%
r 35
5.8%
i 33
5.5%
s 31
5.1%
u 29
4.8%
Other values (11) 132
21.8%
Uppercase Letter
Value Count Frequency (%)
H 31
55.4%
O 9
16.1%
S 5
8.9%
R 4
7.1%
P 4
7.1%
U 2
3.6%
I 1
1.8%
Other Punctuation
Value Count Frequency (%)
/ 8
38.1%
. 8
38.1%
' 5
23.8%
Space Separator
Value Count Frequency (%)
70
100.0%
Open Punctuation
Value Count Frequency (%)
( 8
100.0%
Close Punctuation
Value Count Frequency (%)
) 8
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 661
86.1%
Common 107
13.9%

Most frequent character per script

Latin
Value Count Frequency (%)
e 103
15.6%
o 70
10.6%
t 46
7.0%
n 45
6.8%
m 43
6.5%
a 38
5.7%
r 35
5.3%
i 33
5.0%
s 31
4.7%
H 31
4.7%
Other values (18) 186
28.1%
Common
Value Count Frequency (%)
70
65.4%
( 8
7.5%
/ 8
7.5%
) 8
7.5%
. 8
7.5%
' 5
4.7%

Most occurring blocks

Value Count Frequency (%)
ASCII 768
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 103
13.4%
70
9.1%
o 70
9.1%
t 46
6.0%
n 45
5.9%
m 43
5.6%
a 38
4.9%
r 35
4.6%
i 33
4.3%
s 31
4.0%
Other values (24) 254
33.1%

L03[1]
Categorical

HIGH CORRELATION
MISSING

Please select from the options below the one place where you would most likely be on Thursdays during term time/the semester, at these times of the day. [Before 10:00]

Distinct 6
Distinct (%) 10.3%
Missing 131
Missing (%) 69.3%
Memory size 1.6 KiB
Home
48
University campus
4
Work (other than university)
2
On the go
2
Public Indoor space other than university (e.g. library)
1

Length

Max length 56
Median length 4
Mean length 7.051724138
Min length 4

Characters and Unicode

Total characters 409
Distinct characters 32
Distinct categories 6 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 2 ?
Unique (%) 3.4%

Sample

1st row Home
2nd row Home
3rd row Home
4th row Home
5th row Home

Common Values

Value Count Frequency (%)
Home 48
25.4%
University campus 4
2.1%
Work (other than university) 2
1.1%
On the go 2
1.1%
Public Indoor space other than university (e.g. library) 1
0.5%
Someone else's home 1
0.5%
(Missing) 131
69.3%

Length

2022-07-04T20:14:28.675525 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:28.923832 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
home 49
60.5%
university 7
8.6%
campus 4
4.9%
other 3
3.7%
than 3
3.7%
work 2
2.5%
on 2
2.5%
the 2
2.5%
go 2
2.5%
public 1
1.2%
Other values (6) 6
7.4%

Most occurring characters

Value Count Frequency (%)
e 67
16.4%
o 60
14.7%
m 54
13.2%
H 48
11.7%
23
5.6%
i 16
3.9%
r 15
3.7%
t 15
3.7%
n 14
3.4%
s 14
3.4%
Other values (22) 83
20.3%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 318
77.8%
Uppercase Letter 59
14.4%
Space Separator 23
5.6%
Open Punctuation 3
0.7%
Close Punctuation 3
0.7%
Other Punctuation 3
0.7%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 67
21.1%
o 60
18.9%
m 54
17.0%
i 16
5.0%
r 15
4.7%
t 15
4.7%
n 14
4.4%
s 14
4.4%
h 9
2.8%
a 9
2.8%
Other values (10) 45
14.2%
Uppercase Letter
Value Count Frequency (%)
H 48
81.4%
U 4
6.8%
O 2
3.4%
W 2
3.4%
P 1
1.7%
I 1
1.7%
S 1
1.7%
Other Punctuation
Value Count Frequency (%)
. 2
66.7%
' 1
33.3%
Space Separator
Value Count Frequency (%)
23
100.0%
Open Punctuation
Value Count Frequency (%)
( 3
100.0%
Close Punctuation
Value Count Frequency (%)
) 3
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 377
92.2%
Common 32
7.8%

Most frequent character per script

Latin
Value Count Frequency (%)
e 67
17.8%
o 60
15.9%
m 54
14.3%
H 48
12.7%
i 16
4.2%
r 15
4.0%
t 15
4.0%
n 14
3.7%
s 14
3.7%
h 9
2.4%
Other values (17) 65
17.2%
Common
Value Count Frequency (%)
23
71.9%
( 3
9.4%
) 3
9.4%
. 2
6.2%
' 1
3.1%

Most occurring blocks

Value Count Frequency (%)
ASCII 409
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 67
16.4%
o 60
14.7%
m 54
13.2%
H 48
11.7%
23
5.6%
i 16
3.9%
r 15
3.7%
t 15
3.7%
n 14
3.4%
s 14
3.4%
Other values (22) 83
20.3%

L03[2]
Categorical

HIGH CORRELATION
MISSING

Please select from the options below the one place where you would most likely be on Thursdays during term time/the semester, at these times of the day. [10:00-12:00]

Distinct 7
Distinct (%) 12.1%
Missing 131
Missing (%) 69.3%
Memory size 1.6 KiB
Home
36
University campus
14
Work (other than university)
2
Public Indoor space other than university (e.g. library)
2
Public outdoor place other than university (e.g. park)
2
Other values (2)
2

Length

Max length 56
Median length 4
Mean length 11.82758621
Min length 4

Characters and Unicode

Total characters 686
Distinct characters 32
Distinct categories 6 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 2 ?
Unique (%) 3.4%

Sample

1st row University campus
2nd row Home
3rd row Home
4th row University campus
5th row Home

Common Values

Value Count Frequency (%)
Home 36
19.0%
University campus 14
7.4%
Work (other than university) 2
1.1%
Public Indoor space other than university (e.g. library) 2
1.1%
Public outdoor place other than university (e.g. park) 2
1.1%
On the go 1
0.5%
Someone else's home 1
0.5%
(Missing) 131
69.3%

Length

2022-07-04T20:14:29.181172 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:29.446565 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
home 37
33.6%
university 20
18.2%
campus 14
12.7%
other 6
5.5%
than 6
5.5%
public 4
3.6%
e.g 4
3.6%
indoor 2
1.8%
space 2
1.8%
work 2
1.8%
Other values (9) 13
11.8%

Most occurring characters

Value Count Frequency (%)
e 76
11.1%
o 58
8.5%
52
7.6%
m 52
7.6%
i 46
6.7%
r 38
5.5%
s 38
5.5%
H 36
5.2%
t 35
5.1%
n 30
4.4%
Other values (22) 225
32.8%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 553
80.6%
Uppercase Letter 60
8.7%
Space Separator 52
7.6%
Other Punctuation 9
1.3%
Open Punctuation 6
0.9%
Close Punctuation 6
0.9%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 76
13.7%
o 58
10.5%
m 52
9.4%
i 46
8.3%
r 38
6.9%
s 38
6.9%
t 35
6.3%
n 30
5.4%
a 28
5.1%
u 26
4.7%
Other values (10) 126
22.8%
Uppercase Letter
Value Count Frequency (%)
H 36
60.0%
U 14
23.3%
P 4
6.7%
I 2
3.3%
W 2
3.3%
O 1
1.7%
S 1
1.7%
Other Punctuation
Value Count Frequency (%)
. 8
88.9%
' 1
11.1%
Space Separator
Value Count Frequency (%)
52
100.0%
Open Punctuation
Value Count Frequency (%)
( 6
100.0%
Close Punctuation
Value Count Frequency (%)
) 6
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 613
89.4%
Common 73
10.6%

Most frequent character per script

Latin
Value Count Frequency (%)
e 76
12.4%
o 58
9.5%
m 52
8.5%
i 46
7.5%
r 38
6.2%
s 38
6.2%
H 36
5.9%
t 35
5.7%
n 30
4.9%
a 28
4.6%
Other values (17) 176
28.7%
Common
Value Count Frequency (%)
52
71.2%
. 8
11.0%
( 6
8.2%
) 6
8.2%
' 1
1.4%

Most occurring blocks

Value Count Frequency (%)
ASCII 686
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 76
11.1%
o 58
8.5%
52
7.6%
m 52
7.6%
i 46
6.7%
r 38
5.5%
s 38
5.5%
H 36
5.2%
t 35
5.1%
n 30
4.4%
Other values (22) 225
32.8%

L03[3]
Categorical

HIGH CORRELATION
MISSING

Please select from the options below the one place where you would most likely be on Thursdays during term time/the semester, at these times of the day. [12:00-14:00]

Distinct 8
Distinct (%) 13.8%
Missing 131
Missing (%) 69.3%
Memory size 1.6 KiB
Home
36
University campus
10
Public outdoor place other than university (e.g. park)
4
Restaurant/cafe (including university cafe/restaurant)
3
Work (other than university)
2
Other values (3)
3

Length

Max length 56
Median length 4
Mean length 14.34482759
Min length 4

Characters and Unicode

Total characters 832
Distinct characters 35
Distinct categories 6 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 3 ?
Unique (%) 5.2%

Sample

1st row University campus
2nd row Public outdoor place other than university (e.g. park)
3rd row Home
4th row On the go
5th row Home

Common Values

Value Count Frequency (%)
Home 36
19.0%
University campus 10
5.3%
Public outdoor place other than university (e.g. park) 4
2.1%
Restaurant/cafe (including university cafe/restaurant) 3
1.6%
Work (other than university) 2
1.1%
On the go 1
0.5%
Public Indoor space other than university (e.g. library) 1
0.5%
Someone else's home 1
0.5%
(Missing) 131
69.3%

Length

2022-07-04T20:14:29.731508 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:30.003650 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
home 37
30.3%
university 20
16.4%
campus 10
8.2%
other 7
5.7%
than 7
5.7%
public 5
4.1%
e.g 5
4.1%
outdoor 4
3.3%
place 4
3.3%
park 4
3.3%
Other values (12) 19
15.6%

Most occurring characters

Value Count Frequency (%)
e 91
10.9%
64
7.7%
o 63
7.6%
i 52
6.2%
t 51
6.1%
r 49
5.9%
m 48
5.8%
a 45
5.4%
n 42
5.0%
s 39
4.7%
Other values (25) 288
34.6%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 672
80.8%
Space Separator 64
7.7%
Uppercase Letter 59
7.1%
Other Punctuation 17
2.0%
Open Punctuation 10
1.2%
Close Punctuation 10
1.2%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 91
13.5%
o 63
9.4%
i 52
7.7%
t 51
7.6%
r 49
7.3%
m 48
7.1%
a 45
6.7%
n 42
6.2%
s 39
5.8%
u 38
5.7%
Other values (11) 154
22.9%
Uppercase Letter
Value Count Frequency (%)
H 36
61.0%
U 10
16.9%
P 5
8.5%
R 3
5.1%
W 2
3.4%
O 1
1.7%
I 1
1.7%
S 1
1.7%
Other Punctuation
Value Count Frequency (%)
. 10
58.8%
/ 6
35.3%
' 1
5.9%
Space Separator
Value Count Frequency (%)
64
100.0%
Open Punctuation
Value Count Frequency (%)
( 10
100.0%
Close Punctuation
Value Count Frequency (%)
) 10
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 731
87.9%
Common 101
12.1%

Most frequent character per script

Latin
Value Count Frequency (%)
e 91
12.4%
o 63
8.6%
i 52
7.1%
t 51
7.0%
r 49
6.7%
m 48
6.6%
a 45
6.2%
n 42
5.7%
s 39
5.3%
u 38
5.2%
Other values (19) 213
29.1%
Common
Value Count Frequency (%)
64
63.4%
( 10
9.9%
. 10
9.9%
) 10
9.9%
/ 6
5.9%
' 1
1.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 832
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 91
10.9%
64
7.7%
o 63
7.6%
i 52
6.2%
t 51
6.1%
r 49
5.9%
m 48
5.8%
a 45
5.4%
n 42
5.0%
s 39
4.7%
Other values (25) 288
34.6%

L03[4]
Categorical

HIGH CORRELATION
MISSING

Please select from the options below the one place where you would most likely be on Thursdays during term time/the semester, at these times of the day. [14:00-16:00]

Distinct 8
Distinct (%) 13.8%
Missing 131
Missing (%) 69.3%
Memory size 1.6 KiB
Home
34
University campus
11
Work (other than university)
4
Public Indoor space other than university (e.g. library)
2
Restaurant/cafe (including university cafe/restaurant)
2
Other values (3)
5

Length

Max length 56
Median length 4
Mean length 13.79310345
Min length 4

Characters and Unicode

Total characters 800
Distinct characters 35
Distinct categories 6 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 1 ?
Unique (%) 1.7%

Sample

1st row Home
2nd row Public Indoor space other than university (e.g. library)
3rd row Home
4th row Someone else's home
5th row Home

Common Values

Value Count Frequency (%)
Home 34
18.0%
University campus 11
5.8%
Work (other than university) 4
2.1%
Public Indoor space other than university (e.g. library) 2
1.1%
Restaurant/cafe (including university cafe/restaurant) 2
1.1%
On the go 2
1.1%
Public outdoor place other than university (e.g. park) 2
1.1%
Someone else's home 1
0.5%
(Missing) 131
69.3%

Length

2022-07-04T20:14:30.301359 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:30.574361 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
home 35
28.9%
university 21
17.4%
campus 11
9.1%
other 8
6.6%
than 8
6.6%
work 4
3.3%
public 4
3.3%
e.g 4
3.3%
on 2
1.7%
park 2
1.7%
Other values (12) 22
18.2%

Most occurring characters

Value Count Frequency (%)
e 86
10.8%
63
7.9%
o 61
7.6%
i 52
6.5%
t 49
6.1%
r 49
6.1%
m 47
5.9%
n 42
5.2%
s 40
5.0%
a 39
4.9%
Other values (25) 272
34.0%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 644
80.5%
Space Separator 63
7.9%
Uppercase Letter 60
7.5%
Other Punctuation 13
1.6%
Close Punctuation 10
1.2%
Open Punctuation 10
1.2%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 86
13.4%
o 61
9.5%
i 52
8.1%
t 49
7.6%
r 49
7.6%
m 47
7.3%
n 42
6.5%
s 40
6.2%
a 39
6.1%
u 33
5.1%
Other values (11) 146
22.7%
Uppercase Letter
Value Count Frequency (%)
H 34
56.7%
U 11
18.3%
W 4
6.7%
P 4
6.7%
R 2
3.3%
I 2
3.3%
O 2
3.3%
S 1
1.7%
Other Punctuation
Value Count Frequency (%)
. 8
61.5%
/ 4
30.8%
' 1
7.7%
Space Separator
Value Count Frequency (%)
63
100.0%
Close Punctuation
Value Count Frequency (%)
) 10
100.0%
Open Punctuation
Value Count Frequency (%)
( 10
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 704
88.0%
Common 96
12.0%

Most frequent character per script

Latin
Value Count Frequency (%)
e 86
12.2%
o 61
8.7%
i 52
7.4%
t 49
7.0%
r 49
7.0%
m 47
6.7%
n 42
6.0%
s 40
5.7%
a 39
5.5%
H 34
4.8%
Other values (19) 205
29.1%
Common
Value Count Frequency (%)
63
65.6%
) 10
10.4%
( 10
10.4%
. 8
8.3%
/ 4
4.2%
' 1
1.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 86
10.8%
63
7.9%
o 61
7.6%
i 52
6.5%
t 49
6.1%
r 49
6.1%
m 47
5.9%
n 42
5.2%
s 40
5.0%
a 39
4.9%
Other values (25) 272
34.0%

L03[5]
Categorical

HIGH CORRELATION
MISSING

Please select from the options below the one place where you would most likely be on Thursdays during term time/the semester, at these times of the day. [16:00-18:00]

Distinct 8
Distinct (%) 13.8%
Missing 131
Missing (%) 69.3%
Memory size 1.6 KiB
Home
27
University campus
9
Public outdoor place other than university (e.g. park)
8
On the go
5
Work (other than university)
5
Other values (3)
4

Length

Max length 56
Median length 54
Mean length 18.32758621
Min length 4

Characters and Unicode

Total characters 1063
Distinct characters 35
Distinct categories 6 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 2 ?
Unique (%) 3.4%

Sample

1st row Home
2nd row Public Indoor space other than university (e.g. library)
3rd row On the go
4th row Someone else's home
5th row Home

Common Values

Value Count Frequency (%)
Home 27
14.3%
University campus 9
4.8%
Public outdoor place other than university (e.g. park) 8
4.2%
On the go 5
2.6%
Work (other than university) 5
2.6%
Public Indoor space other than university (e.g. library) 2
1.1%
Someone else's home 1
0.5%
Restaurant/cafe (including university cafe/restaurant) 1
0.5%
(Missing) 131
69.3%

Length

2022-07-04T20:14:30.877276 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:31.147736 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
home 28
16.8%
university 25
15.0%
other 15
9.0%
than 15
9.0%
public 10
6.0%
e.g 10
6.0%
campus 9
5.4%
outdoor 8
4.8%
place 8
4.8%
park 8
4.8%
Other values (12) 31
18.6%

Most occurring characters

Value Count Frequency (%)
109
10.3%
e 101
9.5%
o 83
7.8%
t 72
6.8%
r 70
6.6%
i 64
6.0%
n 52
4.9%
a 50
4.7%
u 46
4.3%
s 40
3.8%
Other values (25) 376
35.4%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 839
78.9%
Space Separator 109
10.3%
Uppercase Letter 60
5.6%
Other Punctuation 23
2.2%
Close Punctuation 16
1.5%
Open Punctuation 16
1.5%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 101
12.0%
o 83
9.9%
t 72
8.6%
r 70
8.3%
i 64
7.6%
n 52
6.2%
a 50
6.0%
u 46
5.5%
s 40
4.8%
m 38
4.5%
Other values (11) 223
26.6%
Uppercase Letter
Value Count Frequency (%)
H 27
45.0%
P 10
16.7%
U 9
15.0%
O 5
8.3%
W 5
8.3%
I 2
3.3%
S 1
1.7%
R 1
1.7%
Other Punctuation
Value Count Frequency (%)
. 20
87.0%
/ 2
8.7%
' 1
4.3%
Space Separator
Value Count Frequency (%)
109
100.0%
Close Punctuation
Value Count Frequency (%)
) 16
100.0%
Open Punctuation
Value Count Frequency (%)
( 16
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 899
84.6%
Common 164
15.4%

Most frequent character per script

Latin
Value Count Frequency (%)
e 101
11.2%
o 83
9.2%
t 72
8.0%
r 70
7.8%
i 64
7.1%
n 52
5.8%
a 50
5.6%
u 46
5.1%
s 40
4.4%
m 38
4.2%
Other values (19) 283
31.5%
Common
Value Count Frequency (%)
109
66.5%
. 20
12.2%
) 16
9.8%
( 16
9.8%
/ 2
1.2%
' 1
0.6%

Most occurring blocks

Value Count Frequency (%)
ASCII 1063
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
109
10.3%
e 101
9.5%
o 83
7.8%
t 72
6.8%
r 70
6.6%
i 64
6.0%
n 52
4.9%
a 50
4.7%
u 46
4.3%
s 40
3.8%
Other values (25) 376
35.4%

L03[6]
Categorical

HIGH CORRELATION
MISSING

Please select from the options below the one place where you would most likely be on Thursdays during term time/the semester, at these times of the day. [After 18:00]

Distinct 8
Distinct (%) 13.8%
Missing 131
Missing (%) 69.3%
Memory size 1.6 KiB
Home
46
Someone else's home
4
On the go
2
Public outdoor place other than university (e.g. park)
2
Public Indoor space other than university (e.g. library)
1
Other values (3)
3

Length

Max length 56
Median length 4
Mean length 9.327586207
Min length 4

Characters and Unicode

Total characters 541
Distinct characters 35
Distinct categories 6 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 4 ?
Unique (%) 6.9%

Sample

1st row Home
2nd row Public Indoor space other than university (e.g. library)
3rd row Home
4th row Someone else's home
5th row Home

Common Values

Value Count Frequency (%)
Home 46
24.3%
Someone else's home 4
2.1%
On the go 2
1.1%
Public outdoor place other than university (e.g. park) 2
1.1%
Public Indoor space other than university (e.g. library) 1
0.5%
Work (other than university) 1
0.5%
Restaurant/cafe (including university cafe/restaurant) 1
0.5%
University campus 1
0.5%
(Missing) 131
69.3%

Length

2022-07-04T20:14:31.452822 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:31.719334 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
home 50
51.0%
university 6
6.1%
else's 4
4.1%
other 4
4.1%
someone 4
4.1%
than 4
4.1%
e.g 3
3.1%
public 3
3.1%
outdoor 2
2.0%
place 2
2.0%
Other values (12) 16
16.3%

Most occurring characters

Value Count Frequency (%)
e 88
16.3%
o 73
13.5%
m 55
10.2%
H 46
8.5%
40
7.4%
t 22
4.1%
r 21
3.9%
n 21
3.9%
i 18
3.3%
s 18
3.3%
Other values (25) 139
25.7%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 420
77.6%
Uppercase Letter 59
10.9%
Space Separator 40
7.4%
Other Punctuation 12
2.2%
Close Punctuation 5
0.9%
Open Punctuation 5
0.9%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 88
21.0%
o 73
17.4%
m 55
13.1%
t 22
5.2%
r 21
5.0%
n 21
5.0%
i 18
4.3%
s 18
4.3%
a 17
4.0%
h 14
3.3%
Other values (11) 73
17.4%
Uppercase Letter
Value Count Frequency (%)
H 46
78.0%
S 4
6.8%
P 3
5.1%
O 2
3.4%
I 1
1.7%
W 1
1.7%
R 1
1.7%
U 1
1.7%
Other Punctuation
Value Count Frequency (%)
. 6
50.0%
' 4
33.3%
/ 2
16.7%
Space Separator
Value Count Frequency (%)
40
100.0%
Close Punctuation
Value Count Frequency (%)
) 5
100.0%
Open Punctuation
Value Count Frequency (%)
( 5
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 479
88.5%
Common 62
11.5%

Most frequent character per script

Latin
Value Count Frequency (%)
e 88
18.4%
o 73
15.2%
m 55
11.5%
H 46
9.6%
t 22
4.6%
r 21
4.4%
n 21
4.4%
i 18
3.8%
s 18
3.8%
a 17
3.5%
Other values (19) 100
20.9%
Common
Value Count Frequency (%)
40
64.5%
. 6
9.7%
) 5
8.1%
( 5
8.1%
' 4
6.5%
/ 2
3.2%

Most occurring blocks

Value Count Frequency (%)
ASCII 541
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 88
16.3%
o 73
13.5%
m 55
10.2%
H 46
8.5%
40
7.4%
t 22
4.1%
r 21
3.9%
n 21
3.9%
i 18
3.3%
s 18
3.3%
Other values (25) 139
25.7%

L04[1]
Categorical

HIGH CORRELATION
MISSING

Please select from the options below the one place where you would most likely be on Sundays during term time/the semester, at these times of the day. [Before 10:00]

Distinct 3
Distinct (%) 5.2%
Missing 131
Missing (%) 69.3%
Memory size 1.6 KiB
Home
55
Someone else's home
2
On the go
1

Length

Max length 19
Median length 4
Mean length 4.603448276
Min length 4

Characters and Unicode

Total characters 267
Distinct characters 14
Distinct categories 4 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 1 ?
Unique (%) 1.7%

Sample

1st row Home
2nd row Home
3rd row Home
4th row Someone else's home
5th row Home

Common Values

Value Count Frequency (%)
Home 55
29.1%
Someone else's home 2
1.1%
On the go 1
0.5%
(Missing) 131
69.3%

Length

2022-07-04T20:14:32.016774 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:32.253626 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
home 57
89.1%
someone 2
3.1%
else's 2
3.1%
on 1
1.6%
the 1
1.6%
go 1
1.6%

Most occurring characters

Value Count Frequency (%)
e 66
24.7%
o 62
23.2%
m 59
22.1%
H 55
20.6%
6
2.2%
s 4
1.5%
n 3
1.1%
h 3
1.1%
S 2
0.7%
l 2
0.7%
Other values (4) 5
1.9%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 201
75.3%
Uppercase Letter 58
21.7%
Space Separator 6
2.2%
Other Punctuation 2
0.7%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 66
32.8%
o 62
30.8%
m 59
29.4%
s 4
2.0%
n 3
1.5%
h 3
1.5%
l 2
1.0%
t 1
0.5%
g 1
0.5%
Uppercase Letter
Value Count Frequency (%)
H 55
94.8%
S 2
3.4%
O 1
1.7%
Space Separator
Value Count Frequency (%)
6
100.0%
Other Punctuation
Value Count Frequency (%)
' 2
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 259
97.0%
Common 8
3.0%

Most frequent character per script

Latin
Value Count Frequency (%)
e 66
25.5%
o 62
23.9%
m 59
22.8%
H 55
21.2%
s 4
1.5%
n 3
1.2%
h 3
1.2%
S 2
0.8%
l 2
0.8%
O 1
0.4%
Other values (2) 2
0.8%
Common
Value Count Frequency (%)
6
75.0%
' 2
25.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 267
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 66
24.7%
o 62
23.2%
m 59
22.1%
H 55
20.6%
6
2.2%
s 4
1.5%
n 3
1.1%
h 3
1.1%
S 2
0.7%
l 2
0.7%
Other values (4) 5
1.9%

L04[2]
Categorical

HIGH CORRELATION
MISSING

Please select from the options below the one place where you would most likely be on Sundays during term time/the semester, at these times of the day. [10:00-12:00]

Distinct 7
Distinct (%) 12.1%
Missing 131
Missing (%) 69.3%
Memory size 1.6 KiB
Home
45
Public outdoor place other than university (e.g. park)
4
Someone else's home
3
Public Indoor space other than university (e.g. library)
2
On the go
2
Other values (2)
2

Length

Max length 56
Median length 4
Mean length 11.46551724
Min length 4

Characters and Unicode

Total characters 665
Distinct characters 34
Distinct categories 6 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 2 ?
Unique (%) 3.4%

Sample

1st row Home
2nd row Home
3rd row Home
4th row Someone else's home
5th row Public Indoor space other than university (e.g. library)

Common Values

Value Count Frequency (%)
Home 45
23.8%
Public outdoor place other than university (e.g. park) 4
2.1%
Someone else's home 3
1.6%
Public Indoor space other than university (e.g. library) 2
1.1%
On the go 2
1.1%
Restaurant/cafe (including university cafe/restaurant) 1
0.5%
Work (other than university) 1
0.5%
(Missing) 131
69.3%

Length

2022-07-04T20:14:32.451603 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:32.946621 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
home 48
41.4%
university 8
6.9%
other 7
6.0%
than 7
6.0%
public 6
5.2%
e.g 6
5.2%
outdoor 4
3.4%
place 4
3.4%
park 4
3.4%
else's 3
2.6%
Other values (11) 19
16.4%

Most occurring characters

Value Count Frequency (%)
e 93
14.0%
o 80
12.0%
58
8.7%
m 51
7.7%
H 45
6.8%
r 33
5.0%
t 32
4.8%
i 26
3.9%
n 26
3.9%
a 25
3.8%
Other values (24) 196
29.5%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 514
77.3%
Uppercase Letter 60
9.0%
Space Separator 58
8.7%
Other Punctuation 17
2.6%
Close Punctuation 8
1.2%
Open Punctuation 8
1.2%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 93
18.1%
o 80
15.6%
m 51
9.9%
r 33
6.4%
t 32
6.2%
i 26
5.1%
n 26
5.1%
a 25
4.9%
u 21
4.1%
h 19
3.7%
Other values (11) 108
21.0%
Uppercase Letter
Value Count Frequency (%)
H 45
75.0%
P 6
10.0%
S 3
5.0%
I 2
3.3%
O 2
3.3%
R 1
1.7%
W 1
1.7%
Other Punctuation
Value Count Frequency (%)
. 12
70.6%
' 3
17.6%
/ 2
11.8%
Space Separator
Value Count Frequency (%)
58
100.0%
Close Punctuation
Value Count Frequency (%)
) 8
100.0%
Open Punctuation
Value Count Frequency (%)
( 8
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 574
86.3%
Common 91
13.7%

Most frequent character per script

Latin
Value Count Frequency (%)
e 93
16.2%
o 80
13.9%
m 51
8.9%
H 45
7.8%
r 33
5.7%
t 32
5.6%
i 26
4.5%
n 26
4.5%
a 25
4.4%
u 21
3.7%
Other values (18) 142
24.7%
Common
Value Count Frequency (%)
58
63.7%
. 12
13.2%
) 8
8.8%
( 8
8.8%
' 3
3.3%
/ 2
2.2%

Most occurring blocks

Value Count Frequency (%)
ASCII 665
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 93
14.0%
o 80
12.0%
58
8.7%
m 51
7.7%
H 45
6.8%
r 33
5.0%
t 32
4.8%
i 26
3.9%
n 26
3.9%
a 25
3.8%
Other values (24) 196
29.5%

L04[3]
Categorical

HIGH CORRELATION
MISSING

Please select from the options below the one place where you would most likely be on Sundays during term time/the semester, at these times of the day. [12:00-14:00]

Distinct 7
Distinct (%) 12.1%
Missing 131
Missing (%) 69.3%
Memory size 1.6 KiB
Home
38
Public outdoor place other than university (e.g. park)
11
Someone else's home
3
Restaurant/cafe (including university cafe/restaurant)
3
On the go
1
Other values (2)
2

Length

Max length 56
Median length 4
Mean length 18.24137931
Min length 4

Characters and Unicode

Total characters 1058
Distinct characters 34
Distinct categories 6 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 3 ?
Unique (%) 5.2%

Sample

1st row On the go
2nd row Public outdoor place other than university (e.g. park)
3rd row Home
4th row Someone else's home
5th row Home

Common Values

Value Count Frequency (%)
Home 38
20.1%
Public outdoor place other than university (e.g. park) 11
5.8%
Someone else's home 3
1.6%
Restaurant/cafe (including university cafe/restaurant) 3
1.6%
On the go 1
0.5%
Public Indoor space other than university (e.g. library) 1
0.5%
Work (other than university) 1
0.5%
(Missing) 131
69.3%

Length

2022-07-04T20:14:33.226690 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:33.488765 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
home 41
25.3%
university 16
9.9%
other 13
8.0%
than 13
8.0%
e.g 12
7.4%
public 12
7.4%
outdoor 11
6.8%
place 11
6.8%
park 11
6.8%
including 3
1.9%
Other values (11) 19
11.7%

Most occurring characters

Value Count Frequency (%)
e 119
11.2%
104
9.8%
o 97
9.2%
t 66
6.2%
r 64
6.0%
a 55
5.2%
i 51
4.8%
u 48
4.5%
n 46
4.3%
m 44
4.2%
Other values (24) 364
34.4%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 830
78.4%
Space Separator 104
9.8%
Uppercase Letter 59
5.6%
Other Punctuation 33
3.1%
Close Punctuation 16
1.5%
Open Punctuation 16
1.5%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 119
14.3%
o 97
11.7%
t 66
8.0%
r 64
7.7%
a 55
6.6%
i 51
6.1%
u 48
5.8%
n 46
5.5%
m 44
5.3%
c 33
4.0%
Other values (11) 207
24.9%
Uppercase Letter
Value Count Frequency (%)
H 38
64.4%
P 12
20.3%
S 3
5.1%
R 3
5.1%
O 1
1.7%
I 1
1.7%
W 1
1.7%
Other Punctuation
Value Count Frequency (%)
. 24
72.7%
/ 6
18.2%
' 3
9.1%
Space Separator
Value Count Frequency (%)
104
100.0%
Close Punctuation
Value Count Frequency (%)
) 16
100.0%
Open Punctuation
Value Count Frequency (%)
( 16
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 889
84.0%
Common 169
16.0%

Most frequent character per script

Latin
Value Count Frequency (%)
e 119
13.4%
o 97
10.9%
t 66
7.4%
r 64
7.2%
a 55
6.2%
i 51
5.7%
u 48
5.4%
n 46
5.2%
m 44
4.9%
H 38
4.3%
Other values (18) 261
29.4%
Common
Value Count Frequency (%)
104
61.5%
. 24
14.2%
) 16
9.5%
( 16
9.5%
/ 6
3.6%
' 3
1.8%

Most occurring blocks

Value Count Frequency (%)
ASCII 1058
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 119
11.2%
104
9.8%
o 97
9.2%
t 66
6.2%
r 64
6.0%
a 55
5.2%
i 51
4.8%
u 48
4.5%
n 46
4.3%
m 44
4.2%
Other values (24) 364
34.4%

L04[4]
Categorical

HIGH CORRELATION
MISSING

Please select from the options below the one place where you would most likely be on Sundays during term time/the semester, at these times of the day. [14:00-16:00]

Distinct 7
Distinct (%) 12.1%
Missing 131
Missing (%) 69.3%
Memory size 1.6 KiB
Home
28
Public outdoor place other than university (e.g. park)
17
On the go
5
Restaurant/cafe (including university cafe/restaurant)
4
Someone else's home
2
Other values (2)
2

Length

Max length 56
Median length 54
Mean length 24.36206897
Min length 4

Characters and Unicode

Total characters 1413
Distinct characters 34
Distinct categories 6 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 2 ?
Unique (%) 3.4%

Sample

1st row On the go
2nd row Public outdoor place other than university (e.g. park)
3rd row On the go
4th row Someone else's home
5th row Home

Common Values

Value Count Frequency (%)
Home 28
14.8%
Public outdoor place other than university (e.g. park) 17
9.0%
On the go 5
2.6%
Restaurant/cafe (including university cafe/restaurant) 4
2.1%
Someone else's home 2
1.1%
Public Indoor space other than university (e.g. library) 1
0.5%
Work (other than university) 1
0.5%
(Missing) 131
69.3%

Length

2022-07-04T20:14:33.775762 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:34.039683 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
home 30
14.1%
university 23
10.8%
other 19
8.9%
than 19
8.9%
public 18
8.5%
e.g 18
8.5%
outdoor 17
8.0%
place 17
8.0%
park 17
8.0%
go 5
2.3%
Other values (11) 30
14.1%

Most occurring characters

Value Count Frequency (%)
155
11.0%
e 137
9.7%
o 112
7.9%
t 99
7.0%
r 92
6.5%
a 79
5.6%
i 73
5.2%
u 70
5.0%
n 66
4.7%
c 48
3.4%
Other values (24) 482
34.1%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1107
78.3%
Space Separator 155
11.0%
Uppercase Letter 59
4.2%
Other Punctuation 46
3.3%
Close Punctuation 23
1.6%
Open Punctuation 23
1.6%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 137
12.4%
o 112
10.1%
t 99
8.9%
r 92
8.3%
a 79
7.1%
i 73
6.6%
u 70
6.3%
n 66
6.0%
c 48
4.3%
h 45
4.1%
Other values (11) 286
25.8%
Uppercase Letter
Value Count Frequency (%)
H 28
47.5%
P 18
30.5%
O 5
8.5%
R 4
6.8%
S 2
3.4%
I 1
1.7%
W 1
1.7%
Other Punctuation
Value Count Frequency (%)
. 36
78.3%
/ 8
17.4%
' 2
4.3%
Space Separator
Value Count Frequency (%)
155
100.0%
Close Punctuation
Value Count Frequency (%)
) 23
100.0%
Open Punctuation
Value Count Frequency (%)
( 23
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1166
82.5%
Common 247
17.5%

Most frequent character per script

Latin
Value Count Frequency (%)
e 137
11.7%
o 112
9.6%
t 99
8.5%
r 92
7.9%
a 79
6.8%
i 73
6.3%
u 70
6.0%
n 66
5.7%
c 48
4.1%
h 45
3.9%
Other values (18) 345
29.6%
Common
Value Count Frequency (%)
155
62.8%
. 36
14.6%
) 23
9.3%
( 23
9.3%
/ 8
3.2%
' 2
0.8%

Most occurring blocks

Value Count Frequency (%)
ASCII 1413
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
155
11.0%
e 137
9.7%
o 112
7.9%
t 99
7.0%
r 92
6.5%
a 79
5.6%
i 73
5.2%
u 70
5.0%
n 66
4.7%
c 48
3.4%
Other values (24) 482
34.1%

L04[5]
Categorical

HIGH CORRELATION
MISSING

Please select from the options below the one place where you would most likely be on Sundays during term time/the semester, at these times of the day. [16:00-18:00]

Distinct 7
Distinct (%) 12.1%
Missing 131
Missing (%) 69.3%
Memory size 1.6 KiB
Home
27
Public outdoor place other than university (e.g. park)
14
On the go
6
Restaurant/cafe (including university cafe/restaurant)
4
Someone else's home
3
Other values (2)
4

Length

Max length 56
Median length 54
Mean length 23.43103448
Min length 4

Characters and Unicode

Total characters 1359
Distinct characters 34
Distinct categories 6 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row On the go
2nd row Public outdoor place other than university (e.g. park)
3rd row On the go
4th row On the go
5th row Home

Common Values

Value Count Frequency (%)
Home 27
14.3%
Public outdoor place other than university (e.g. park) 14
7.4%
On the go 6
3.2%
Restaurant/cafe (including university cafe/restaurant) 4
2.1%
Someone else's home 3
1.6%
Public Indoor space other than university (e.g. library) 2
1.1%
Work (other than university) 2
1.1%
(Missing) 131
69.3%

Length

2022-07-04T20:14:34.348751 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:34.617328 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
home 30
14.6%
university 22
10.7%
other 18
8.7%
than 18
8.7%
public 16
7.8%
e.g 16
7.8%
outdoor 14
6.8%
place 14
6.8%
park 14
6.8%
go 6
2.9%
Other values (11) 38
18.4%

Most occurring characters

Value Count Frequency (%)
148
10.9%
e 136
10.0%
o 108
7.9%
t 94
6.9%
r 88
6.5%
a 74
5.4%
i 70
5.2%
n 67
4.9%
u 64
4.7%
h 45
3.3%
Other values (24) 465
34.2%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1064
78.3%
Space Separator 148
10.9%
Uppercase Letter 60
4.4%
Other Punctuation 43
3.2%
Close Punctuation 22
1.6%
Open Punctuation 22
1.6%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 136
12.8%
o 108
10.2%
t 94
8.8%
r 88
8.3%
a 74
7.0%
i 70
6.6%
n 67
6.3%
u 64
6.0%
h 45
4.2%
c 44
4.1%
Other values (11) 274
25.8%
Uppercase Letter
Value Count Frequency (%)
H 27
45.0%
P 16
26.7%
O 6
10.0%
R 4
6.7%
S 3
5.0%
I 2
3.3%
W 2
3.3%
Other Punctuation
Value Count Frequency (%)
. 32
74.4%
/ 8
18.6%
' 3
7.0%
Space Separator
Value Count Frequency (%)
148
100.0%
Close Punctuation
Value Count Frequency (%)
) 22
100.0%
Open Punctuation
Value Count Frequency (%)
( 22
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1124
82.7%
Common 235
17.3%

Most frequent character per script

Latin
Value Count Frequency (%)
e 136
12.1%
o 108
9.6%
t 94
8.4%
r 88
7.8%
a 74
6.6%
i 70
6.2%
n 67
6.0%
u 64
5.7%
h 45
4.0%
c 44
3.9%
Other values (18) 334
29.7%
Common
Value Count Frequency (%)
148
63.0%
. 32
13.6%
) 22
9.4%
( 22
9.4%
/ 8
3.4%
' 3
1.3%

Most occurring blocks

Value Count Frequency (%)
ASCII 1359
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
148
10.9%
e 136
10.0%
o 108
7.9%
t 94
6.9%
r 88
6.5%
a 74
5.4%
i 70
5.2%
n 67
4.9%
u 64
4.7%
h 45
3.3%
Other values (24) 465
34.2%

L04[6]
Categorical

HIGH CORRELATION
MISSING

Please select from the options below the one place where you would most likely be on Sundays during term time/the semester, at these times of the day. [After 18:00]

Distinct 7
Distinct (%) 12.1%
Missing 131
Missing (%) 69.3%
Memory size 1.6 KiB
Home
40
Someone else's home
5
Restaurant/cafe (including university cafe/restaurant)
5
On the go
3
Public outdoor place other than university (e.g. park)
2
Other values (2)
3

Length

Max length 56
Median length 4
Mean length 13.31034483
Min length 4

Characters and Unicode

Total characters 772
Distinct characters 34
Distinct categories 6 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 1 ?
Unique (%) 1.7%

Sample

1st row Home
2nd row Public outdoor place other than university (e.g. park)
3rd row Home
4th row Someone else's home
5th row Home

Common Values

Value Count Frequency (%)
Home 40
21.2%
Someone else's home 5
2.6%
Restaurant/cafe (including university cafe/restaurant) 5
2.6%
On the go 3
1.6%
Public outdoor place other than university (e.g. park) 2
1.1%
Work (other than university) 2
1.1%
Public Indoor space other than university (e.g. library) 1
0.5%
(Missing) 131
69.3%

Length

2022-07-04T20:14:34.910847 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:35.169972 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
home 45
38.8%
university 10
8.6%
someone 5
4.3%
else's 5
4.3%
restaurant/cafe 5
4.3%
including 5
4.3%
cafe/restaurant 5
4.3%
than 5
4.3%
other 5
4.3%
e.g 3
2.6%
Other values (11) 23
19.8%

Most occurring characters

Value Count Frequency (%)
e 109
14.1%
o 73
9.5%
58
7.5%
m 50
6.5%
t 45
5.8%
n 44
5.7%
a 41
5.3%
H 40
5.2%
r 39
5.1%
i 34
4.4%
Other values (24) 239
31.0%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 614
79.5%
Uppercase Letter 59
7.6%
Space Separator 58
7.5%
Other Punctuation 21
2.7%
Close Punctuation 10
1.3%
Open Punctuation 10
1.3%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 109
17.8%
o 73
11.9%
m 50
8.1%
t 45
7.3%
n 44
7.2%
a 41
6.7%
r 39
6.4%
i 34
5.5%
s 31
5.0%
u 30
4.9%
Other values (11) 118
19.2%
Uppercase Letter
Value Count Frequency (%)
H 40
67.8%
R 5
8.5%
S 5
8.5%
O 3
5.1%
P 3
5.1%
W 2
3.4%
I 1
1.7%
Other Punctuation
Value Count Frequency (%)
/ 10
47.6%
. 6
28.6%
' 5
23.8%
Space Separator
Value Count Frequency (%)
58
100.0%
Close Punctuation
Value Count Frequency (%)
) 10
100.0%
Open Punctuation
Value Count Frequency (%)
( 10
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 673
87.2%
Common 99
12.8%

Most frequent character per script

Latin
Value Count Frequency (%)
e 109
16.2%
o 73
10.8%
m 50
7.4%
t 45
6.7%
n 44
6.5%
a 41
6.1%
H 40
5.9%
r 39
5.8%
i 34
5.1%
s 31
4.6%
Other values (18) 167
24.8%
Common
Value Count Frequency (%)
58
58.6%
) 10
10.1%
/ 10
10.1%
( 10
10.1%
. 6
6.1%
' 5
5.1%

Most occurring blocks

Value Count Frequency (%)
ASCII 772
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 109
14.1%
o 73
9.5%
58
7.5%
m 50
6.5%
t 45
5.8%
n 44
5.7%
a 41
5.3%
H 40
5.2%
r 39
5.1%
i 34
4.4%
Other values (24) 239
31.0%

TS01[1]
Boolean

HIGH CORRELATION
MISSING

Was there a particular time of day when it was useful to put questions into the chatbot? [In the morning]

Distinct 2
Distinct (%) 1.1%
Missing 15
Missing (%) 7.9%
Memory size 506.0 B
False
129
True
45
(Missing)
15
Value Count Frequency (%)
False 129
68.3%
True 45
23.8%
(Missing) 15
7.9%
2022-07-04T20:14:35.481622 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

TS01[2]
Boolean

HIGH CORRELATION
MISSING

Was there a particular time of day when it was useful to put questions into the chatbot? [Around noon]

Distinct 2
Distinct (%) 1.1%
Missing 15
Missing (%) 7.9%
Memory size 506.0 B
False
137
True
37
(Missing)
15
Value Count Frequency (%)
False 137
72.5%
True 37
19.6%
(Missing) 15
7.9%
2022-07-04T20:14:35.705168 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

TS01[3]
Boolean

HIGH CORRELATION
MISSING

Was there a particular time of day when it was useful to put questions into the chatbot? [In the afternoon]

Distinct 2
Distinct (%) 1.1%
Missing 15
Missing (%) 7.9%
Memory size 506.0 B
False
111
True
63
(Missing)
15
Value Count Frequency (%)
False 111
58.7%
True 63
33.3%
(Missing) 15
7.9%
2022-07-04T20:14:35.927445 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

TS01[4]
Boolean

HIGH CORRELATION
MISSING

Was there a particular time of day when it was useful to put questions into the chatbot? [In the evening]

Distinct 2
Distinct (%) 1.1%
Missing 15
Missing (%) 7.9%
Memory size 506.0 B
True
91
False
83
(Missing)
15
Value Count Frequency (%)
True 91
48.1%
False 83
43.9%
(Missing) 15
7.9%
2022-07-04T20:14:36.148177 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

TS01[5]
Boolean

HIGH CORRELATION
MISSING

Was there a particular time of day when it was useful to put questions into the chatbot? [At night]

Distinct 2
Distinct (%) 1.1%
Missing 15
Missing (%) 7.9%
Memory size 506.0 B
False
132
True
42
(Missing)
15
Value Count Frequency (%)
False 132
69.8%
True 42
22.2%
(Missing) 15
7.9%
2022-07-04T20:14:36.371720 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

TS02[1]
Boolean

HIGH CORRELATION
MISSING

From which locations was it convenient to put questions to the chatbot? [At home]

Distinct 2
Distinct (%) 1.1%
Missing 15
Missing (%) 7.9%
Memory size 506.0 B
True
168
False
6
(Missing)
15
Value Count Frequency (%)
True 168
88.9%
False 6
3.2%
(Missing) 15
7.9%
2022-07-04T20:14:36.589071 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

TS02[2]
Boolean

HIGH CORRELATION
MISSING

From which locations was it convenient to put questions to the chatbot? [At someone else's home]

Distinct 2
Distinct (%) 1.1%
Missing 15
Missing (%) 7.9%
Memory size 506.0 B
False
163
True
11
(Missing)
15
Value Count Frequency (%)
False 163
86.2%
True 11
5.8%
(Missing) 15
7.9%
2022-07-04T20:14:36.794826 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

TS02[3]
Boolean

HIGH CORRELATION
MISSING

From which locations was it convenient to put questions to the chatbot? [On the Uni campus]

Distinct 2
Distinct (%) 1.1%
Missing 15
Missing (%) 7.9%
Memory size 506.0 B
False
156
True
18
(Missing)
15
Value Count Frequency (%)
False 156
82.5%
True 18
9.5%
(Missing) 15
7.9%
2022-07-04T20:14:37.010578 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

TS02[4]
Boolean

HIGH CORRELATION
MISSING

From which locations was it convenient to put questions to the chatbot? [At work other than Uni]

Distinct 2
Distinct (%) 1.1%
Missing 15
Missing (%) 7.9%
Memory size 506.0 B
False
164
True
10
(Missing)
15
Value Count Frequency (%)
False 164
86.8%
True 10
5.3%
(Missing) 15
7.9%
2022-07-04T20:14:37.228644 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

TS02[5]
Boolean

HIGH CORRELATION
MISSING

From which locations was it convenient to put questions to the chatbot? [Café/restaurant]

Distinct 2
Distinct (%) 1.1%
Missing 15
Missing (%) 7.9%
Memory size 506.0 B
False
167
True
7
(Missing)
15
Value Count Frequency (%)
False 167
88.4%
True 7
3.7%
(Missing) 15
7.9%
2022-07-04T20:14:37.445752 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

TS02[6]
Boolean

HIGH CORRELATION
MISSING

From which locations was it convenient to put questions to the chatbot? [Public outdoor place (not Uni)]

Distinct 2
Distinct (%) 1.1%
Missing 15
Missing (%) 7.9%
Memory size 506.0 B
False
162
True
12
(Missing)
15
Value Count Frequency (%)
False 162
85.7%
True 12
6.3%
(Missing) 15
7.9%
2022-07-04T20:14:37.660581 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

TS02[7]
Boolean

HIGH CORRELATION
MISSING

From which locations was it convenient to put questions to the chatbot? [Public indoor place (not Uni)]

Distinct 2
Distinct (%) 1.1%
Missing 15
Missing (%) 7.9%
Memory size 506.0 B
False
161
True
13
(Missing)
15
Value Count Frequency (%)
False 161
85.2%
True 13
6.9%
(Missing) 15
7.9%
2022-07-04T20:14:37.871068 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

TS02[8]
Boolean

HIGH CORRELATION
MISSING

From which locations was it convenient to put questions to the chatbot? [On the go]

Distinct 2
Distinct (%) 1.1%
Missing 15
Missing (%) 7.9%
Memory size 506.0 B
False
150
True
24
(Missing)
15
Value Count Frequency (%)
False 150
79.4%
True 24
12.7%
(Missing) 15
7.9%
2022-07-04T20:14:38.084877 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

TS02[9]
Boolean

HIGH CORRELATION
MISSING

From which locations was it convenient to put questions to the chatbot? [Other places]

Distinct 2
Distinct (%) 1.1%
Missing 15
Missing (%) 7.9%
Memory size 506.0 B
False
166
True
8
(Missing)
15
Value Count Frequency (%)
False 166
87.8%
True 8
4.2%
(Missing) 15
7.9%
2022-07-04T20:14:38.307894 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

B01
Unsupported

MISSING
REJECTED
UNSUPPORTED

Here is an example badge: “Congratulations! You just earned the First Question badge! Way to go!”

Missing 189
Missing (%) 100.0%
Memory size 1.6 KiB

B02
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Please write in at least one badge you received

Distinct 146
Distinct (%) 86.4%
Missing 20
Missing (%) 10.6%
Memory size 1.6 KiB
You keep giving great answers! You just got Good Answers Level 2! Congratulations!
5
Good Answers Level 2
4
Level 2 Helper
4
Level 2 Curious
3
First Question
2
Other values (141)
151

Length

Max length 182
Median length 82
Mean length 37.26627219
Min length 1

Characters and Unicode

Total characters 6298
Distinct characters 108
Distinct categories 10 ?
Distinct scripts 3 ?
Distinct blocks 4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 131 ?
Unique (%) 77.5%

Sample

1st row recibí la de preguntas, la de respuesta, la de entregar buenas respuestas, 2 de cada una creo
2nd row First Question Badge, First Answer Badge, First Good Answer Badge, Level One Curious, Level One Helper
3rd row no recuerdo si recibi algna
4th row Una de oro, creo que era por tener una buena respuesta o algo asi
5th row level 2

Common Values

Value Count Frequency (%)
You keep giving great answers! You just got Good Answers Level 2! Congratulations! 5
2.6%
Good Answers Level 2 4
2.1%
Level 2 Helper 4
2.1%
Level 2 Curious 3
1.6%
First Question 2
1.1%
асуулт 2
1.1%
First Good Answer 2
1.1%
First Answer badge 2
1.1%
First Good Answer badge 2
1.1%
Congratulations! You just earned the First Answer badge! Way to go! 2
1.1%
Other values (136) 141
74.6%
(Missing) 20
10.6%

Length

2022-07-04T20:14:38.579186 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
Value Count Frequency (%)
level 68
6.4%
badge 62
5.8%
good 45
4.2%
2 43
4.0%
answer 38
3.6%
answers 37
3.5%
first 36
3.4%
1 30
2.8%
you 27
2.5%
question 25
2.4%
Other values (282) 652
61.3%

Most occurring characters

Value Count Frequency (%)
911
14.5%
e 654
10.4%
s 378
6.0%
o 356
5.7%
a 350
5.6%
r 332
5.3%
n 310
4.9%
t 266
4.2%
i 251
4.0%
d 215
3.4%
Other values (98) 2275
36.1%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 4762
75.6%
Space Separator 911
14.5%
Uppercase Letter 357
5.7%
Other Punctuation 156
2.5%
Decimal Number 98
1.6%
Dash Punctuation 5
0.1%
Close Punctuation 3
< 0.1%
Open Punctuation 3
< 0.1%
Final Punctuation 2
< 0.1%
Initial Punctuation 1
< 0.1%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 654
13.7%
s 378
7.9%
o 356
7.5%
a 350
7.3%
r 332
7.0%
n 310
6.5%
t 266
5.6%
i 251
5.3%
d 215
4.5%
l 191
4.0%
Other values (46) 1459
30.6%
Uppercase Letter
Value Count Frequency (%)
L 59
16.5%
A 46
12.9%
G 43
12.0%
C 37
10.4%
F 35
9.8%
Y 27
7.6%
B 21
5.9%
H 15
4.2%
I 15
4.2%
Q 14
3.9%
Other values (18) 45
12.6%
Other Punctuation
Value Count Frequency (%)
! 61
39.1%
, 58
37.2%
. 12
7.7%
' 9
5.8%
? 4
2.6%
" 4
2.6%
/ 3
1.9%
; 3
1.9%
¡ 2
1.3%
Decimal Number
Value Count Frequency (%)
2 47
48.0%
1 37
37.8%
3 4
4.1%
5 3
3.1%
0 3
3.1%
4 1
1.0%
6 1
1.0%
9 1
1.0%
7 1
1.0%
Space Separator
Value Count Frequency (%)
911
100.0%
Dash Punctuation
Value Count Frequency (%)
- 5
100.0%
Close Punctuation
Value Count Frequency (%)
) 3
100.0%
Open Punctuation
Value Count Frequency (%)
( 3
100.0%
Final Punctuation
Value Count Frequency (%)
” 2
100.0%
Initial Punctuation
Value Count Frequency (%)
“ 1
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 4644
73.7%
Common 1179
18.7%
Cyrillic 475
7.5%

Most frequent character per script

Latin
Value Count Frequency (%)
e 654
14.1%
s 378
8.1%
o 356
7.7%
a 350
7.5%
r 332
7.1%
n 310
6.7%
t 266
5.7%
i 251
5.4%
d 215
4.6%
l 191
4.1%
Other values (39) 1341
28.9%
Cyrillic
Value Count Frequency (%)
а 71
14.9%
э 40
8.4%
н 39
8.2%
г 32
6.7%
у 29
6.1%
л 29
6.1%
т 28
5.9%
с 24
5.1%
й 22
4.6%
р 20
4.2%
Other values (25) 141
29.7%
Common
Value Count Frequency (%)
911
77.3%
! 61
5.2%
, 58
4.9%
2 47
4.0%
1 37
3.1%
. 12
1.0%
' 9
0.8%
- 5
0.4%
3 4
0.3%
? 4
0.3%
Other values (14) 31
2.6%

Most occurring blocks

Value Count Frequency (%)
ASCII 5815
92.3%
Cyrillic 475
7.5%
None 5
0.1%
Punctuation 3
< 0.1%

Most frequent character per block

ASCII
Value Count Frequency (%)
911
15.7%
e 654
11.2%
s 378
6.5%
o 356
6.1%
a 350
6.0%
r 332
5.7%
n 310
5.3%
t 266
4.6%
i 251
4.3%
d 215
3.7%
Other values (58) 1792
30.8%
Cyrillic
Value Count Frequency (%)
а 71
14.9%
э 40
8.4%
н 39
8.2%
г 32
6.7%
у 29
6.1%
л 29
6.1%
т 28
5.9%
с 24
5.1%
й 22
4.6%
р 20
4.2%
Other values (25) 141
29.7%
None
Value Count Frequency (%)
è 2
40.0%
¡ 2
40.0%
í 1
20.0%
Punctuation
Value Count Frequency (%)
” 2
66.7%
“ 1
33.3%

B03[1]
Categorical

HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with these statements [I liked the chatbot's badges]

Distinct 5
Distinct (%) 2.9%
Missing 19
Missing (%) 10.1%
Memory size 1.6 KiB
Agree
59
Neither agree or disagree
44
Strongly agree
42
Disagree
14
Strongly disagree
11

Length

Max length 25
Median length 17
Mean length 13.42352941
Min length 5

Characters and Unicode

Total characters 2282
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Agree
2nd row Strongly agree
3rd row Neither agree or disagree
4th row Neither agree or disagree
5th row Agree

Common Values

Value Count Frequency (%)
Agree 59
31.2%
Neither agree or disagree 44
23.3%
Strongly agree 42
22.2%
Disagree 14
7.4%
Strongly disagree 11
5.8%
(Missing) 19
10.1%

Length

2022-07-04T20:14:38.883551 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:39.173125 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 145
40.8%
disagree 69
19.4%
strongly 53
14.9%
neither 44
12.4%
or 44
12.4%

Most occurring characters

Value Count Frequency (%)
e 516
22.6%
r 355
15.6%
g 267
11.7%
185
8.1%
a 155
6.8%
i 113
5.0%
t 97
4.3%
o 97
4.3%
s 69
3.0%
A 59
2.6%
Other values (8) 369
16.2%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1927
84.4%
Space Separator 185
8.1%
Uppercase Letter 170
7.4%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 516
26.8%
r 355
18.4%
g 267
13.9%
a 155
8.0%
i 113
5.9%
t 97
5.0%
o 97
5.0%
s 69
3.6%
d 55
2.9%
n 53
2.8%
Other values (3) 150
7.8%
Uppercase Letter
Value Count Frequency (%)
A 59
34.7%
S 53
31.2%
N 44
25.9%
D 14
8.2%
Space Separator
Value Count Frequency (%)
185
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 2097
91.9%
Common 185
8.1%

Most frequent character per script

Latin
Value Count Frequency (%)
e 516
24.6%
r 355
16.9%
g 267
12.7%
a 155
7.4%
i 113
5.4%
t 97
4.6%
o 97
4.6%
s 69
3.3%
A 59
2.8%
d 55
2.6%
Other values (7) 314
15.0%
Common
Value Count Frequency (%)
185
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 2282
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 516
22.6%
r 355
15.6%
g 267
11.7%
185
8.1%
a 155
6.8%
i 113
5.0%
t 97
4.3%
o 97
4.3%
s 69
3.0%
A 59
2.6%
Other values (8) 369
16.2%

B03[2]
Categorical

HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with these statements [The badges were a distraction]

Distinct 5
Distinct (%) 2.9%
Missing 19
Missing (%) 10.1%
Memory size 1.6 KiB
Disagree
56
Neither agree or disagree
50
Strongly disagree
46
Agree
15
Strongly agree
3

Length

Max length 25
Median length 17
Mean length 15.27647059
Min length 5

Characters and Unicode

Total characters 2597
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Strongly disagree
2nd row Strongly disagree
3rd row Agree
4th row Disagree
5th row Neither agree or disagree

Common Values

Value Count Frequency (%)
Disagree 56
29.6%
Neither agree or disagree 50
26.5%
Strongly disagree 46
24.3%
Agree 15
7.9%
Strongly agree 3
1.6%
(Missing) 19
10.1%

Length

2022-07-04T20:14:39.447985 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:39.974298 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
disagree 152
41.2%
agree 68
18.4%
neither 50
13.6%
or 50
13.6%
strongly 49
13.3%

Most occurring characters

Value Count Frequency (%)
e 540
20.8%
r 369
14.2%
g 269
10.4%
a 205
7.9%
i 202
7.8%
199
7.7%
s 152
5.9%
o 99
3.8%
t 99
3.8%
d 96
3.7%
Other values (8) 367
14.1%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 2228
85.8%
Space Separator 199
7.7%
Uppercase Letter 170
6.5%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 540
24.2%
r 369
16.6%
g 269
12.1%
a 205
9.2%
i 202
9.1%
s 152
6.8%
o 99
4.4%
t 99
4.4%
d 96
4.3%
h 50
2.2%
Other values (3) 147
6.6%
Uppercase Letter
Value Count Frequency (%)
D 56
32.9%
N 50
29.4%
S 49
28.8%
A 15
8.8%
Space Separator
Value Count Frequency (%)
199
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 2398
92.3%
Common 199
7.7%

Most frequent character per script

Latin
Value Count Frequency (%)
e 540
22.5%
r 369
15.4%
g 269
11.2%
a 205
8.5%
i 202
8.4%
s 152
6.3%
o 99
4.1%
t 99
4.1%
d 96
4.0%
D 56
2.3%
Other values (7) 311
13.0%
Common
Value Count Frequency (%)
199
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 2597
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 540
20.8%
r 369
14.2%
g 269
10.4%
a 205
7.9%
i 202
7.8%
199
7.7%
s 152
5.9%
o 99
3.8%
t 99
3.8%
d 96
3.7%
Other values (8) 367
14.1%

B03[3]
Categorical

HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with these statements [The badges enhanced the chatbot experience]

Distinct 5
Distinct (%) 2.9%
Missing 19
Missing (%) 10.1%
Memory size 1.6 KiB
Agree
53
Neither agree or disagree
45
Strongly agree
33
Disagree
25
Strongly disagree
14

Length

Max length 25
Median length 17
Mean length 13.47058824
Min length 5

Characters and Unicode

Total characters 2290
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Strongly agree
2nd row Strongly agree
3rd row Neither agree or disagree
4th row Neither agree or disagree
5th row Neither agree or disagree

Common Values

Value Count Frequency (%)
Agree 53
28.0%
Neither agree or disagree 45
23.8%
Strongly agree 33
17.5%
Disagree 25
13.2%
Strongly disagree 14
7.4%
(Missing) 19
10.1%

Length

2022-07-04T20:14:40.250443 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:40.544672 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 131
37.2%
disagree 84
23.9%
strongly 47
13.4%
neither 45
12.8%
or 45
12.8%

Most occurring characters

Value Count Frequency (%)
e 520
22.7%
r 352
15.4%
g 262
11.4%
182
7.9%
a 162
7.1%
i 129
5.6%
o 92
4.0%
t 92
4.0%
s 84
3.7%
d 59
2.6%
Other values (8) 356
15.5%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1938
84.6%
Space Separator 182
7.9%
Uppercase Letter 170
7.4%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 520
26.8%
r 352
18.2%
g 262
13.5%
a 162
8.4%
i 129
6.7%
o 92
4.7%
t 92
4.7%
s 84
4.3%
d 59
3.0%
n 47
2.4%
Other values (3) 139
7.2%
Uppercase Letter
Value Count Frequency (%)
A 53
31.2%
S 47
27.6%
N 45
26.5%
D 25
14.7%
Space Separator
Value Count Frequency (%)
182
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 2108
92.1%
Common 182
7.9%

Most frequent character per script

Latin
Value Count Frequency (%)
e 520
24.7%
r 352
16.7%
g 262
12.4%
a 162
7.7%
i 129
6.1%
o 92
4.4%
t 92
4.4%
s 84
4.0%
d 59
2.8%
A 53
2.5%
Other values (7) 303
14.4%
Common
Value Count Frequency (%)
182
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 2290
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 520
22.7%
r 352
15.4%
g 262
11.4%
182
7.9%
a 162
7.1%
i 129
5.6%
o 92
4.0%
t 92
4.0%
s 84
3.7%
d 59
2.6%
Other values (8) 356
15.5%

B03[4]
Categorical

HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with these statements [The badges encouraged me to contribute to the chatbot]

Distinct 5
Distinct (%) 2.9%
Missing 19
Missing (%) 10.1%
Memory size 1.6 KiB
Agree
55
Neither agree or disagree
36
Strongly agree
32
Disagree
31
Strongly disagree
16

Length

Max length 25
Median length 17
Mean length 12.60588235
Min length 5

Characters and Unicode

Total characters 2143
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Strongly agree
2nd row Neither agree or disagree
3rd row Neither agree or disagree
4th row Neither agree or disagree
5th row Disagree

Common Values

Value Count Frequency (%)
Agree 55
29.1%
Neither agree or disagree 36
19.0%
Strongly agree 32
16.9%
Disagree 31
16.4%
Strongly disagree 16
8.5%
(Missing) 19
10.1%

Length

2022-07-04T20:14:40.818242 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:41.102153 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 123
37.7%
disagree 83
25.5%
strongly 48
14.7%
neither 36
11.0%
or 36
11.0%

Most occurring characters

Value Count Frequency (%)
e 484
22.6%
r 326
15.2%
g 254
11.9%
156
7.3%
a 151
7.0%
i 119
5.6%
t 84
3.9%
o 84
3.9%
s 83
3.9%
A 55
2.6%
Other values (8) 347
16.2%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1817
84.8%
Uppercase Letter 170
7.9%
Space Separator 156
7.3%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 484
26.6%
r 326
17.9%
g 254
14.0%
a 151
8.3%
i 119
6.5%
t 84
4.6%
o 84
4.6%
s 83
4.6%
d 52
2.9%
n 48
2.6%
Other values (3) 132
7.3%
Uppercase Letter
Value Count Frequency (%)
A 55
32.4%
S 48
28.2%
N 36
21.2%
D 31
18.2%
Space Separator
Value Count Frequency (%)
156
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1987
92.7%
Common 156
7.3%

Most frequent character per script

Latin
Value Count Frequency (%)
e 484
24.4%
r 326
16.4%
g 254
12.8%
a 151
7.6%
i 119
6.0%
t 84
4.2%
o 84
4.2%
s 83
4.2%
A 55
2.8%
d 52
2.6%
Other values (7) 295
14.8%
Common
Value Count Frequency (%)
156
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 2143
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 484
22.6%
r 326
15.2%
g 254
11.9%
156
7.3%
a 151
7.0%
i 119
5.6%
t 84
3.9%
o 84
3.9%
s 83
3.9%
A 55
2.6%
Other values (8) 347
16.2%

B03[5]
Categorical

HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with these statements [Chatbot should be more generous with badges]

Distinct 5
Distinct (%) 2.9%
Missing 19
Missing (%) 10.1%
Memory size 1.6 KiB
Neither agree or disagree
65
Agree
41
Disagree
31
Strongly agree
19
Strongly disagree
14

Length

Max length 25
Median length 17
Mean length 15.18823529
Min length 5

Characters and Unicode

Total characters 2582
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Disagree
2nd row Disagree
3rd row Neither agree or disagree
4th row Agree
5th row Neither agree or disagree

Common Values

Value Count Frequency (%)
Neither agree or disagree 65
34.4%
Agree 41
21.7%
Disagree 31
16.4%
Strongly agree 19
10.1%
Strongly disagree 14
7.4%
(Missing) 19
10.1%

Length

2022-07-04T20:14:41.375688 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:41.659654 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 125
31.4%
disagree 110
27.6%
neither 65
16.3%
or 65
16.3%
strongly 33
8.3%

Most occurring characters

Value Count Frequency (%)
e 600
23.2%
r 398
15.4%
g 268
10.4%
228
8.8%
a 194
7.5%
i 175
6.8%
s 110
4.3%
o 98
3.8%
t 98
3.8%
d 79
3.1%
Other values (8) 334
12.9%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 2184
84.6%
Space Separator 228
8.8%
Uppercase Letter 170
6.6%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 600
27.5%
r 398
18.2%
g 268
12.3%
a 194
8.9%
i 175
8.0%
s 110
5.0%
o 98
4.5%
t 98
4.5%
d 79
3.6%
h 65
3.0%
Other values (3) 99
4.5%
Uppercase Letter
Value Count Frequency (%)
N 65
38.2%
A 41
24.1%
S 33
19.4%
D 31
18.2%
Space Separator
Value Count Frequency (%)
228
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 2354
91.2%
Common 228
8.8%

Most frequent character per script

Latin
Value Count Frequency (%)
e 600
25.5%
r 398
16.9%
g 268
11.4%
a 194
8.2%
i 175
7.4%
s 110
4.7%
o 98
4.2%
t 98
4.2%
d 79
3.4%
N 65
2.8%
Other values (7) 269
11.4%
Common
Value Count Frequency (%)
228
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 2582
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 600
23.2%
r 398
15.4%
g 268
10.4%
228
8.8%
a 194
7.5%
i 175
6.8%
s 110
4.3%
o 98
3.8%
t 98
3.8%
d 79
3.1%
Other values (8) 334
12.9%

B03[6]
Categorical

HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with these statements [More type of badges should be used]

Distinct 5
Distinct (%) 2.9%
Missing 19
Missing (%) 10.1%
Memory size 1.6 KiB
Agree
56
Neither agree or disagree
51
Strongly agree
28
Disagree
24
Strongly disagree
11

Length

Max length 25
Median length 17
Mean length 13.68235294
Min length 5

Characters and Unicode

Total characters 2326
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Neither agree or disagree
2nd row Disagree
3rd row Neither agree or disagree
4th row Agree
5th row Neither agree or disagree

Common Values

Value Count Frequency (%)
Agree 56
29.6%
Neither agree or disagree 51
27.0%
Strongly agree 28
14.8%
Disagree 24
12.7%
Strongly disagree 11
5.8%
(Missing) 19
10.1%

Length

2022-07-04T20:14:41.940286 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:42.229614 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 135
37.3%
disagree 86
23.8%
neither 51
14.1%
or 51
14.1%
strongly 39
10.8%

Most occurring characters

Value Count Frequency (%)
e 544
23.4%
r 362
15.6%
g 260
11.2%
192
8.3%
a 165
7.1%
i 137
5.9%
o 90
3.9%
t 90
3.9%
s 86
3.7%
d 62
2.7%
Other values (8) 338
14.5%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1964
84.4%
Space Separator 192
8.3%
Uppercase Letter 170
7.3%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 544
27.7%
r 362
18.4%
g 260
13.2%
a 165
8.4%
i 137
7.0%
o 90
4.6%
t 90
4.6%
s 86
4.4%
d 62
3.2%
h 51
2.6%
Other values (3) 117
6.0%
Uppercase Letter
Value Count Frequency (%)
A 56
32.9%
N 51
30.0%
S 39
22.9%
D 24
14.1%
Space Separator
Value Count Frequency (%)
192
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 2134
91.7%
Common 192
8.3%

Most frequent character per script

Latin
Value Count Frequency (%)
e 544
25.5%
r 362
17.0%
g 260
12.2%
a 165
7.7%
i 137
6.4%
o 90
4.2%
t 90
4.2%
s 86
4.0%
d 62
2.9%
A 56
2.6%
Other values (7) 282
13.2%
Common
Value Count Frequency (%)
192
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 2326
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 544
23.4%
r 362
15.6%
g 260
11.2%
192
8.3%
a 165
7.1%
i 137
5.9%
o 90
3.9%
t 90
3.9%
s 86
3.7%
d 62
2.7%
Other values (8) 338
14.5%

B03[7]
Categorical

HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with these statements [Badges based on the acceptance of answers should be used more]

Distinct 5
Distinct (%) 2.9%
Missing 19
Missing (%) 10.1%
Memory size 1.6 KiB
Agree
60
Neither agree or disagree
55
Strongly agree
23
Disagree
22
Strongly disagree
10

Length

Max length 25
Median length 17
Mean length 13.78235294
Min length 5

Characters and Unicode

Total characters 2343
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Agree
2nd row Agree
3rd row Neither agree or disagree
4th row Agree
5th row Agree

Common Values

Value Count Frequency (%)
Agree 60
31.7%
Neither agree or disagree 55
29.1%
Strongly agree 23
12.2%
Disagree 22
11.6%
Strongly disagree 10
5.3%
(Missing) 19
10.1%

Length

2022-07-04T20:14:42.505592 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:42.797374 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 138
37.5%
disagree 87
23.6%
neither 55
14.9%
or 55
14.9%
strongly 33
9.0%

Most occurring characters

Value Count Frequency (%)
e 560
23.9%
r 368
15.7%
g 258
11.0%
198
8.5%
a 165
7.0%
i 142
6.1%
o 88
3.8%
t 88
3.8%
s 87
3.7%
d 65
2.8%
Other values (8) 324
13.8%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1975
84.3%
Space Separator 198
8.5%
Uppercase Letter 170
7.3%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 560
28.4%
r 368
18.6%
g 258
13.1%
a 165
8.4%
i 142
7.2%
o 88
4.5%
t 88
4.5%
s 87
4.4%
d 65
3.3%
h 55
2.8%
Other values (3) 99
5.0%
Uppercase Letter
Value Count Frequency (%)
A 60
35.3%
N 55
32.4%
S 33
19.4%
D 22
12.9%
Space Separator
Value Count Frequency (%)
198
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 2145
91.5%
Common 198
8.5%

Most frequent character per script

Latin
Value Count Frequency (%)
e 560
26.1%
r 368
17.2%
g 258
12.0%
a 165
7.7%
i 142
6.6%
o 88
4.1%
t 88
4.1%
s 87
4.1%
d 65
3.0%
A 60
2.8%
Other values (7) 264
12.3%
Common
Value Count Frequency (%)
198
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 2343
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 560
23.9%
r 368
15.7%
g 258
11.0%
198
8.5%
a 165
7.0%
i 142
6.1%
o 88
3.8%
t 88
3.8%
s 87
3.7%
d 65
2.8%
Other values (8) 324
13.8%

M01
Unsupported

MISSING
REJECTED
UNSUPPORTED

Here is an example message: “You haven't asked a question yet. You can get help from the community with your questions. Type /question to ask the community!”

Missing 189
Missing (%) 100.0%
Memory size 1.6 KiB

M02
Categorical

HIGH CORRELATION
MISSING
UNIFORM

Please write in at least one message you received.

Distinct 50
Distinct (%) 92.6%
Missing 135
Missing (%) 71.4%
Memory size 1.6 KiB
You haven't asked a question recently. Anything you are wondering about that the community may know?
4
Lo sapevi? I piani per il fine settimana sono spesso fatti attorno al giovedì. E i tuoi? Chiedi ai tuoi compagni per avere qualche idea!
2
I’m very sorry, but I didn’t understand ???? Write /info to learn how I can assist!
1
I cant remember
1
You haven't asked a question yet. You can get help from the community with your questions. Type /question to ask the community!
1
Other values (45)
45

Length

Max length 144
Median length 89.5
Mean length 76.12962963
Min length 2

Characters and Unicode

Total characters 4111
Distinct characters 65
Distinct categories 9 ?
Distinct scripts 2 ?
Distinct blocks 3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 48 ?
Unique (%) 88.9%

Sample

1st row Grazie! Ora invio la tua risposta alla persona interessata
2nd row You haven't asked a question recently. Anything you are wondering about that the community may know?
3rd row Consigli su come capire se un esame fosse in presenza o meno
4th row È un po' di tempo che non fai una domanda
5th row Lo sapevi? Alcuni dei tuoi compagni sono appasionati di musica. Vuoi saperne di più? Fai una domanda!

Common Values

Value Count Frequency (%)
You haven't asked a question recently. Anything you are wondering about that the community may know? 4
2.1%
Lo sapevi? I piani per il fine settimana sono spesso fatti attorno al giovedì. E i tuoi? Chiedi ai tuoi compagni per avere qualche idea! 2
1.1%
I’m very sorry, but I didn’t understand ???? Write /info to learn how I can assist! 1
0.5%
I cant remember 1
0.5%
You haven't asked a question yet. You can get help from the community with your questions. Type /question to ask the community! 1
0.5%
Most students are part of a student organization, ask about it 1
0.5%
Did you know? Weekend plans are often made around Thursday. How about yours? Ask your peers to get some ideas! 1
0.5%
You haven't asked a question yet. You can get help from the community with your questions. Type /question to ask the community 1
0.5%
You are 1 question/s away from a new badge! Type /question to ask the community! 1
0.5%
Flexibility 1
0.5%
Other values (40) 40
21.2%
(Missing) 135
71.4%

Length

2022-07-04T20:14:43.104529 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
Value Count Frequency (%)
the 26
3.6%
you 25
3.4%
a 23
3.2%
ask 21
2.9%
to 20
2.8%
question 18
2.5%
are 18
2.5%
community 16
2.2%
i 14
1.9%
about 12
1.7%
Other values (277) 532
73.4%

Most occurring characters

Value Count Frequency (%)
678
16.5%
e 376
9.1%
a 302
7.3%
t 285
6.9%
o 282
6.9%
i 251
6.1%
n 234
5.7%
s 204
5.0%
r 163
4.0%
u 152
3.7%
Other values (55) 1184
28.8%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 3197
77.8%
Space Separator 678
16.5%
Other Punctuation 113
2.7%
Uppercase Letter 109
2.7%
Decimal Number 5
0.1%
Final Punctuation 3
0.1%
Open Punctuation 2
< 0.1%
Close Punctuation 2
< 0.1%
Dash Punctuation 2
< 0.1%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 376
11.8%
a 302
9.4%
t 285
8.9%
o 282
8.8%
i 251
7.9%
n 234
7.3%
s 204
6.4%
r 163
5.1%
u 152
4.8%
d 115
3.6%
Other values (19) 833
26.1%
Uppercase Letter
Value Count Frequency (%)
Y 15
13.8%
A 15
13.8%
T 13
11.9%
I 11
10.1%
P 8
7.3%
L 7
6.4%
C 6
5.5%
W 6
5.5%
S 5
4.6%
E 5
4.6%
Other values (10) 18
16.5%
Other Punctuation
Value Count Frequency (%)
? 33
29.2%
. 27
23.9%
! 23
20.4%
/ 13
11.5%
' 10
8.8%
, 5
4.4%
" 2
1.8%
Decimal Number
Value Count Frequency (%)
1 2
40.0%
3 1
20.0%
7 1
20.0%
4 1
20.0%
Space Separator
Value Count Frequency (%)
678
100.0%
Final Punctuation
Value Count Frequency (%)
’ 3
100.0%
Open Punctuation
Value Count Frequency (%)
( 2
100.0%
Close Punctuation
Value Count Frequency (%)
) 2
100.0%
Dash Punctuation
Value Count Frequency (%)
- 2
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 3306
80.4%
Common 805
19.6%

Most frequent character per script

Latin
Value Count Frequency (%)
e 376
11.4%
a 302
9.1%
t 285
8.6%
o 282
8.5%
i 251
7.6%
n 234
7.1%
s 204
6.2%
r 163
4.9%
u 152
4.6%
d 115
3.5%
Other values (39) 942
28.5%
Common
Value Count Frequency (%)
678
84.2%
? 33
4.1%
. 27
3.4%
! 23
2.9%
/ 13
1.6%
' 10
1.2%
, 5
0.6%
’ 3
0.4%
( 2
0.2%
) 2
0.2%
Other values (6) 9
1.1%

Most occurring blocks

Value Count Frequency (%)
ASCII 4100
99.7%
None 8
0.2%
Punctuation 3
0.1%

Most frequent character per block

ASCII
Value Count Frequency (%)
678
16.5%
e 376
9.2%
a 302
7.4%
t 285
7.0%
o 282
6.9%
i 251
6.1%
n 234
5.7%
s 204
5.0%
r 163
4.0%
u 152
3.7%
Other values (49) 1173
28.6%
Punctuation
Value Count Frequency (%)
’ 3
100.0%
None
Value Count Frequency (%)
ì 3
37.5%
à 2
25.0%
ù 1
12.5%
è 1
12.5%
È 1
12.5%

M03[1]
Categorical

HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with these statements. [I liked the chatbot’s messages]

Distinct 5
Distinct (%) 9.3%
Missing 135
Missing (%) 71.4%
Memory size 1.6 KiB
Neither agree or disagree
25
Agree
15
Disagree
8
Strongly disagree
3
Strongly agree
3

Length

Max length 25
Median length 17
Mean length 15.87037037
Min length 5

Characters and Unicode

Total characters 857
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Strongly disagree
2nd row Neither agree or disagree
3rd row Neither agree or disagree
4th row Neither agree or disagree
5th row Neither agree or disagree

Common Values

Value Count Frequency (%)
Neither agree or disagree 25
13.2%
Agree 15
7.9%
Disagree 8
4.2%
Strongly disagree 3
1.6%
Strongly agree 3
1.6%
(Missing) 135
71.4%

Length

2022-07-04T20:14:43.389336 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:43.652795 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 43
31.9%
disagree 36
26.7%
neither 25
18.5%
or 25
18.5%
strongly 6
4.4%

Most occurring characters

Value Count Frequency (%)
e 208
24.3%
r 135
15.8%
g 85
9.9%
81
9.5%
a 64
7.5%
i 61
7.1%
s 36
4.2%
o 31
3.6%
t 31
3.6%
d 28
3.3%
Other values (8) 97
11.3%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 722
84.2%
Space Separator 81
9.5%
Uppercase Letter 54
6.3%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 208
28.8%
r 135
18.7%
g 85
11.8%
a 64
8.9%
i 61
8.4%
s 36
5.0%
o 31
4.3%
t 31
4.3%
d 28
3.9%
h 25
3.5%
Other values (3) 18
2.5%
Uppercase Letter
Value Count Frequency (%)
N 25
46.3%
A 15
27.8%
D 8
14.8%
S 6
11.1%
Space Separator
Value Count Frequency (%)
81
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 776
90.5%
Common 81
9.5%

Most frequent character per script

Latin
Value Count Frequency (%)
e 208
26.8%
r 135
17.4%
g 85
11.0%
a 64
8.2%
i 61
7.9%
s 36
4.6%
o 31
4.0%
t 31
4.0%
d 28
3.6%
N 25
3.2%
Other values (7) 72
9.3%
Common
Value Count Frequency (%)
81
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 857
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 208
24.3%
r 135
15.8%
g 85
9.9%
81
9.5%
a 64
7.5%
i 61
7.1%
s 36
4.2%
o 31
3.6%
t 31
3.6%
d 28
3.3%
Other values (8) 97
11.3%

M03[2]
Categorical

HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with these statements. [The messages enhanced the chatbot experience]

Distinct 5
Distinct (%) 9.3%
Missing 135
Missing (%) 71.4%
Memory size 1.6 KiB
Neither agree or disagree
21
Agree
17
Disagree
10
Strongly disagree
3
Strongly agree
3

Length

Max length 25
Median length 17
Mean length 14.5
Min length 5

Characters and Unicode

Total characters 783
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Strongly disagree
2nd row Neither agree or disagree
3rd row Agree
4th row Neither agree or disagree
5th row Neither agree or disagree

Common Values

Value Count Frequency (%)
Neither agree or disagree 21
11.1%
Agree 17
9.0%
Disagree 10
5.3%
Strongly disagree 3
1.6%
Strongly agree 3
1.6%
(Missing) 135
71.4%

Length

2022-07-04T20:14:43.904098 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:44.175486 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 41
33.3%
disagree 34
27.6%
neither 21
17.1%
or 21
17.1%
strongly 6
4.9%

Most occurring characters

Value Count Frequency (%)
e 192
24.5%
r 123
15.7%
g 81
10.3%
69
8.8%
a 58
7.4%
i 55
7.0%
s 34
4.3%
o 27
3.4%
t 27
3.4%
d 24
3.1%
Other values (8) 93
11.9%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 660
84.3%
Space Separator 69
8.8%
Uppercase Letter 54
6.9%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 192
29.1%
r 123
18.6%
g 81
12.3%
a 58
8.8%
i 55
8.3%
s 34
5.2%
o 27
4.1%
t 27
4.1%
d 24
3.6%
h 21
3.2%
Other values (3) 18
2.7%
Uppercase Letter
Value Count Frequency (%)
N 21
38.9%
A 17
31.5%
D 10
18.5%
S 6
11.1%
Space Separator
Value Count Frequency (%)
69
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 714
91.2%
Common 69
8.8%

Most frequent character per script

Latin
Value Count Frequency (%)
e 192
26.9%
r 123
17.2%
g 81
11.3%
a 58
8.1%
i 55
7.7%
s 34
4.8%
o 27
3.8%
t 27
3.8%
d 24
3.4%
N 21
2.9%
Other values (7) 72
10.1%
Common
Value Count Frequency (%)
69
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 783
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 192
24.5%
r 123
15.7%
g 81
10.3%
69
8.8%
a 58
7.4%
i 55
7.0%
s 34
4.3%
o 27
3.4%
t 27
3.4%
d 24
3.1%
Other values (8) 93
11.9%

M03[3]
Categorical

HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with these statements. [The messages were a distraction]

Distinct 5
Distinct (%) 9.3%
Missing 135
Missing (%) 71.4%
Memory size 1.6 KiB
Neither agree or disagree
20
Disagree
20
Strongly disagree
6
Agree
5
Strongly agree
3

Length

Max length 25
Median length 17
Mean length 15.35185185
Min length 5

Characters and Unicode

Total characters 829
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Neither agree or disagree
2nd row Agree
3rd row Disagree
4th row Neither agree or disagree
5th row Neither agree or disagree

Common Values

Value Count Frequency (%)
Neither agree or disagree 20
10.6%
Disagree 20
10.6%
Strongly disagree 6
3.2%
Agree 5
2.6%
Strongly agree 3
1.6%
(Missing) 135
71.4%

Length

2022-07-04T20:14:44.423866 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:44.684278 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
disagree 46
37.4%
agree 28
22.8%
neither 20
16.3%
or 20
16.3%
strongly 9
7.3%

Most occurring characters

Value Count Frequency (%)
e 188
22.7%
r 123
14.8%
g 83
10.0%
69
8.3%
a 69
8.3%
i 66
8.0%
s 46
5.5%
o 29
3.5%
t 29
3.5%
d 26
3.1%
Other values (8) 101
12.2%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 706
85.2%
Space Separator 69
8.3%
Uppercase Letter 54
6.5%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 188
26.6%
r 123
17.4%
g 83
11.8%
a 69
9.8%
i 66
9.3%
s 46
6.5%
o 29
4.1%
t 29
4.1%
d 26
3.7%
h 20
2.8%
Other values (3) 27
3.8%
Uppercase Letter
Value Count Frequency (%)
D 20
37.0%
N 20
37.0%
S 9
16.7%
A 5
9.3%
Space Separator
Value Count Frequency (%)
69
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 760
91.7%
Common 69
8.3%

Most frequent character per script

Latin
Value Count Frequency (%)
e 188
24.7%
r 123
16.2%
g 83
10.9%
a 69
9.1%
i 66
8.7%
s 46
6.1%
o 29
3.8%
t 29
3.8%
d 26
3.4%
D 20
2.6%
Other values (7) 81
10.7%
Common
Value Count Frequency (%)
69
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 829
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 188
22.7%
r 123
14.8%
g 83
10.0%
69
8.3%
a 69
8.3%
i 66
8.0%
s 46
5.5%
o 29
3.5%
t 29
3.5%
d 26
3.1%
Other values (8) 101
12.2%

M03[4]
Categorical

HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with these statements. [The messages encouraged me to contribute to chatbot]

Distinct 5
Distinct (%) 9.3%
Missing 135
Missing (%) 71.4%
Memory size 1.6 KiB
Agree
22
Disagree
14
Neither agree or disagree
11
Strongly agree
5
Strongly disagree
2

Length

Max length 25
Median length 17
Mean length 11.12962963
Min length 5

Characters and Unicode

Total characters 601
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Neither agree or disagree
2nd row Disagree
3rd row Neither agree or disagree
4th row Disagree
5th row Disagree

Common Values

Value Count Frequency (%)
Agree 22
11.6%
Disagree 14
7.4%
Neither agree or disagree 11
5.8%
Strongly agree 5
2.6%
Strongly disagree 2
1.1%
(Missing) 135
71.4%

Length

2022-07-04T20:14:44.944252 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:45.219378 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 38
40.4%
disagree 27
28.7%
neither 11
11.7%
or 11
11.7%
strongly 7
7.4%

Most occurring characters

Value Count Frequency (%)
e 152
25.3%
r 94
15.6%
g 72
12.0%
a 43
7.2%
40
6.7%
i 38
6.3%
s 27
4.5%
A 22
3.7%
o 18
3.0%
t 18
3.0%
Other values (8) 77
12.8%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 507
84.4%
Uppercase Letter 54
9.0%
Space Separator 40
6.7%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 152
30.0%
r 94
18.5%
g 72
14.2%
a 43
8.5%
i 38
7.5%
s 27
5.3%
o 18
3.6%
t 18
3.6%
d 13
2.6%
h 11
2.2%
Other values (3) 21
4.1%
Uppercase Letter
Value Count Frequency (%)
A 22
40.7%
D 14
25.9%
N 11
20.4%
S 7
13.0%
Space Separator
Value Count Frequency (%)
40
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 561
93.3%
Common 40
6.7%

Most frequent character per script

Latin
Value Count Frequency (%)
e 152
27.1%
r 94
16.8%
g 72
12.8%
a 43
7.7%
i 38
6.8%
s 27
4.8%
A 22
3.9%
o 18
3.2%
t 18
3.2%
D 14
2.5%
Other values (7) 63
11.2%
Common
Value Count Frequency (%)
40
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 601
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 152
25.3%
r 94
15.6%
g 72
12.0%
a 43
7.2%
40
6.7%
i 38
6.3%
s 27
4.5%
A 22
3.7%
o 18
3.0%
t 18
3.0%
Other values (8) 77
12.8%

M03[5]
Categorical

HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with these statements. [More types of messages should be used]

Distinct 5
Distinct (%) 9.3%
Missing 135
Missing (%) 71.4%
Memory size 1.6 KiB
Neither agree or disagree
25
Agree
14
Disagree
8
Strongly disagree
5
Strongly agree
2

Length

Max length 25
Median length 17
Mean length 16.14814815
Min length 5

Characters and Unicode

Total characters 872
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Strongly disagree
2nd row Disagree
3rd row Agree
4th row Neither agree or disagree
5th row Neither agree or disagree

Common Values

Value Count Frequency (%)
Neither agree or disagree 25
13.2%
Agree 14
7.4%
Disagree 8
4.2%
Strongly disagree 5
2.6%
Strongly agree 2
1.1%
(Missing) 135
71.4%

Length

2022-07-04T20:14:45.474991 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:45.742106 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 41
30.1%
disagree 38
27.9%
neither 25
18.4%
or 25
18.4%
strongly 7
5.1%

Most occurring characters

Value Count Frequency (%)
e 208
23.9%
r 136
15.6%
g 86
9.9%
82
9.4%
a 65
7.5%
i 63
7.2%
s 38
4.4%
o 32
3.7%
t 32
3.7%
d 30
3.4%
Other values (8) 100
11.5%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 736
84.4%
Space Separator 82
9.4%
Uppercase Letter 54
6.2%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 208
28.3%
r 136
18.5%
g 86
11.7%
a 65
8.8%
i 63
8.6%
s 38
5.2%
o 32
4.3%
t 32
4.3%
d 30
4.1%
h 25
3.4%
Other values (3) 21
2.9%
Uppercase Letter
Value Count Frequency (%)
N 25
46.3%
A 14
25.9%
D 8
14.8%
S 7
13.0%
Space Separator
Value Count Frequency (%)
82
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 790
90.6%
Common 82
9.4%

Most frequent character per script

Latin
Value Count Frequency (%)
e 208
26.3%
r 136
17.2%
g 86
10.9%
a 65
8.2%
i 63
8.0%
s 38
4.8%
o 32
4.1%
t 32
4.1%
d 30
3.8%
N 25
3.2%
Other values (7) 75
9.5%
Common
Value Count Frequency (%)
82
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 872
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 208
23.9%
r 136
15.6%
g 86
9.9%
82
9.4%
a 65
7.5%
i 63
7.2%
s 38
4.4%
o 32
3.7%
t 32
3.7%
d 30
3.4%
Other values (8) 100
11.5%

M03[6]
Categorical

HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with these statements. [Messages should be sent less frequently]

Distinct 5
Distinct (%) 9.3%
Missing 135
Missing (%) 71.4%
Memory size 1.6 KiB
Neither agree or disagree
22
Agree
14
Disagree
10
Strongly agree
7
Strongly disagree
1

Length

Max length 25
Median length 17
Mean length 15.09259259
Min length 5

Characters and Unicode

Total characters 815
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 1 ?
Unique (%) 1.9%

Sample

1st row Strongly agree
2nd row Agree
3rd row Agree
4th row Neither agree or disagree
5th row Agree

Common Values

Value Count Frequency (%)
Neither agree or disagree 22
11.6%
Agree 14
7.4%
Disagree 10
5.3%
Strongly agree 7
3.7%
Strongly disagree 1
0.5%
(Missing) 135
71.4%

Length

2022-07-04T20:14:46.003955 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:46.268154 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 43
33.6%
disagree 33
25.8%
neither 22
17.2%
or 22
17.2%
strongly 8
6.2%

Most occurring characters

Value Count Frequency (%)
e 196
24.0%
r 128
15.7%
g 84
10.3%
74
9.1%
a 62
7.6%
i 55
6.7%
s 33
4.0%
o 30
3.7%
t 30
3.7%
d 23
2.8%
Other values (8) 100
12.3%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 687
84.3%
Space Separator 74
9.1%
Uppercase Letter 54
6.6%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 196
28.5%
r 128
18.6%
g 84
12.2%
a 62
9.0%
i 55
8.0%
s 33
4.8%
o 30
4.4%
t 30
4.4%
d 23
3.3%
h 22
3.2%
Other values (3) 24
3.5%
Uppercase Letter
Value Count Frequency (%)
N 22
40.7%
A 14
25.9%
D 10
18.5%
S 8
14.8%
Space Separator
Value Count Frequency (%)
74
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 741
90.9%
Common 74
9.1%

Most frequent character per script

Latin
Value Count Frequency (%)
e 196
26.5%
r 128
17.3%
g 84
11.3%
a 62
8.4%
i 55
7.4%
s 33
4.5%
o 30
4.0%
t 30
4.0%
d 23
3.1%
N 22
3.0%
Other values (7) 78
10.5%
Common
Value Count Frequency (%)
74
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 815
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 196
24.0%
r 128
15.7%
g 84
10.3%
74
9.1%
a 62
7.6%
i 55
6.7%
s 33
4.0%
o 30
3.7%
t 30
3.7%
d 23
2.8%
Other values (8) 100
12.3%

M03[7]
Categorical

HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with these statements. [Messages should be personalised for each user]

Distinct 5
Distinct (%) 9.3%
Missing 135
Missing (%) 71.4%
Memory size 1.6 KiB
Agree
24
Neither agree or disagree
18
Strongly agree
5
Disagree
5
Strongly disagree
2

Length

Max length 25
Median length 17
Mean length 13.22222222
Min length 5

Characters and Unicode

Total characters 714
Distinct characters 18
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Strongly agree
2nd row Disagree
3rd row Agree
4th row Disagree
5th row Agree

Common Values

Value Count Frequency (%)
Agree 24
12.7%
Neither agree or disagree 18
9.5%
Strongly agree 5
2.6%
Disagree 5
2.6%
Strongly disagree 2
1.1%
(Missing) 135
71.4%

Length

2022-07-04T20:14:46.528352 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:47.037329 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 47
40.9%
disagree 25
21.7%
neither 18
15.7%
or 18
15.7%
strongly 7
6.1%

Most occurring characters

Value Count Frequency (%)
e 180
25.2%
r 115
16.1%
g 79
11.1%
61
8.5%
a 48
6.7%
i 43
6.0%
s 25
3.5%
t 25
3.5%
o 25
3.5%
A 24
3.4%
Other values (8) 89
12.5%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 599
83.9%
Space Separator 61
8.5%
Uppercase Letter 54
7.6%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 180
30.1%
r 115
19.2%
g 79
13.2%
a 48
8.0%
i 43
7.2%
s 25
4.2%
t 25
4.2%
o 25
4.2%
d 20
3.3%
h 18
3.0%
Other values (3) 21
3.5%
Uppercase Letter
Value Count Frequency (%)
A 24
44.4%
N 18
33.3%
S 7
13.0%
D 5
9.3%
Space Separator
Value Count Frequency (%)
61
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 653
91.5%
Common 61
8.5%

Most frequent character per script

Latin
Value Count Frequency (%)
e 180
27.6%
r 115
17.6%
g 79
12.1%
a 48
7.4%
i 43
6.6%
s 25
3.8%
t 25
3.8%
o 25
3.8%
A 24
3.7%
d 20
3.1%
Other values (7) 69
10.6%
Common
Value Count Frequency (%)
61
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 714
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 180
25.2%
r 115
16.1%
g 79
11.1%
61
8.5%
a 48
6.7%
i 43
6.0%
s 25
3.5%
t 25
3.5%
o 25
3.5%
A 24
3.4%
Other values (8) 89
12.5%

UX02[1]
Categorical

HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with the following statements. [The chatbot helped me to acquire new ideas]

Distinct 9
Distinct (%) 5.4%
Missing 21
Missing (%) 11.1%
Memory size 1.6 KiB
Agree
61
Neither agree or disagree
40
Strongly agree
22
Disagree
20
De acuerdo
12
Other values (4)
13

Length

Max length 30
Median length 25
Mean length 13.01785714
Min length 5

Characters and Unicode

Total characters 2187
Distinct characters 23
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 1 ?
Unique (%) 0.6%

Sample

1st row De acuerdo
2nd row De acuerdo
3rd row Totalmente de acuerdo
4th row Ni de acuerdo ni en desacuerdo
5th row Ni de acuerdo ni en desacuerdo

Common Values

Value Count Frequency (%)
Agree 61
32.3%
Neither agree or disagree 40
21.2%
Strongly agree 22
11.6%
Disagree 20
10.6%
De acuerdo 12
6.3%
Ni de acuerdo ni en desacuerdo 5
2.6%
Strongly disagree 4
2.1%
Totalmente de acuerdo 3
1.6%
En desacuerdo 1
0.5%
(Missing) 21
11.1%

Length

2022-07-04T20:14:47.282911 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:47.572138 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 123
34.4%
disagree 64
17.9%
neither 40
11.2%
or 40
11.2%
strongly 26
7.3%
de 20
5.6%
acuerdo 20
5.6%
ni 10
2.8%
en 6
1.7%
desacuerdo 6
1.7%

Most occurring characters

Value Count Frequency (%)
e 517
23.6%
r 319
14.6%
g 213
9.7%
190
8.7%
a 155
7.1%
i 114
5.2%
o 95
4.3%
d 84
3.8%
t 72
3.3%
s 70
3.2%
Other values (13) 358
16.4%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1829
83.6%
Space Separator 190
8.7%
Uppercase Letter 168
7.7%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 517
28.3%
r 319
17.4%
g 213
11.6%
a 155
8.5%
i 114
6.2%
o 95
5.2%
d 84
4.6%
t 72
3.9%
s 70
3.8%
h 40
2.2%
Other values (6) 150
8.2%
Uppercase Letter
Value Count Frequency (%)
A 61
36.3%
N 45
26.8%
D 32
19.0%
S 26
15.5%
T 3
1.8%
E 1
0.6%
Space Separator
Value Count Frequency (%)
190
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1997
91.3%
Common 190
8.7%

Most frequent character per script

Latin
Value Count Frequency (%)
e 517
25.9%
r 319
16.0%
g 213
10.7%
a 155
7.8%
i 114
5.7%
o 95
4.8%
d 84
4.2%
t 72
3.6%
s 70
3.5%
A 61
3.1%
Other values (12) 297
14.9%
Common
Value Count Frequency (%)
190
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 2187
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 517
23.6%
r 319
14.6%
g 213
9.7%
190
8.7%
a 155
7.1%
i 114
5.2%
o 95
4.3%
d 84
3.8%
t 72
3.3%
s 70
3.2%
Other values (13) 358
16.4%

UX02[2]
Categorical

HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with the following statements. [The chatbot was useful to reach out for help ]

Distinct 9
Distinct (%) 5.4%
Missing 21
Missing (%) 11.1%
Memory size 1.6 KiB
Agree
77
Strongly agree
32
Neither agree or disagree
23
Disagree
13
De acuerdo
12
Other values (4)
11

Length

Max length 30
Median length 25
Mean length 11.31547619
Min length 5

Characters and Unicode

Total characters 1901
Distinct characters 23
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 1 ?
Unique (%) 0.6%

Sample

1st row De acuerdo
2nd row De acuerdo
3rd row Ni de acuerdo ni en desacuerdo
4th row En desacuerdo
5th row Ni de acuerdo ni en desacuerdo

Common Values

Value Count Frequency (%)
Agree 77
40.7%
Strongly agree 32
16.9%
Neither agree or disagree 23
12.2%
Disagree 13
6.9%
De acuerdo 12
6.3%
Ni de acuerdo ni en desacuerdo 6
3.2%
Totalmente de acuerdo 2
1.1%
Strongly disagree 2
1.1%
En desacuerdo 1
0.5%
(Missing) 21
11.1%

Length

2022-07-04T20:14:47.890458 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:48.188925 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 132
41.5%
disagree 38
11.9%
strongly 34
10.7%
neither 23
7.2%
or 23
7.2%
de 20
6.3%
acuerdo 20
6.3%
ni 12
3.8%
en 7
2.2%
desacuerdo 7
2.2%

Most occurring characters

Value Count Frequency (%)
e 450
23.7%
r 277
14.6%
g 204
10.7%
150
7.9%
a 122
6.4%
o 86
4.5%
A 77
4.1%
i 73
3.8%
d 67
3.5%
t 61
3.2%
Other values (13) 334
17.6%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1583
83.3%
Uppercase Letter 168
8.8%
Space Separator 150
7.9%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 450
28.4%
r 277
17.5%
g 204
12.9%
a 122
7.7%
o 86
5.4%
i 73
4.6%
d 67
4.2%
t 61
3.9%
n 49
3.1%
s 45
2.8%
Other values (6) 149
9.4%
Uppercase Letter
Value Count Frequency (%)
A 77
45.8%
S 34
20.2%
N 29
17.3%
D 25
14.9%
T 2
1.2%
E 1
0.6%
Space Separator
Value Count Frequency (%)
150
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1751
92.1%
Common 150
7.9%

Most frequent character per script

Latin
Value Count Frequency (%)
e 450
25.7%
r 277
15.8%
g 204
11.7%
a 122
7.0%
o 86
4.9%
A 77
4.4%
i 73
4.2%
d 67
3.8%
t 61
3.5%
n 49
2.8%
Other values (12) 285
16.3%
Common
Value Count Frequency (%)
150
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1901
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 450
23.7%
r 277
14.6%
g 204
10.7%
150
7.9%
a 122
6.4%
o 86
4.5%
A 77
4.1%
i 73
3.8%
d 67
3.5%
t 61
3.2%
Other values (13) 334
17.6%

UX02[3]
Categorical

HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with the following statements. [The chatbot was useful to provide help to others.]

Distinct 8
Distinct (%) 4.8%
Missing 21
Missing (%) 11.1%
Memory size 1.6 KiB
Agree
86
Strongly agree
40
De acuerdo
13
Neither agree or disagree
12
Totalmente de acuerdo
7
Other values (3)
10

Length

Max length 30
Median length 5
Mean length 10.04166667
Min length 5

Characters and Unicode

Total characters 1687
Distinct characters 22
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 1 ?
Unique (%) 0.6%

Sample

1st row Totalmente de acuerdo
2nd row De acuerdo
3rd row Totalmente de acuerdo
4th row De acuerdo
5th row De acuerdo

Common Values

Value Count Frequency (%)
Agree 86
45.5%
Strongly agree 40
21.2%
De acuerdo 13
6.9%
Neither agree or disagree 12
6.3%
Totalmente de acuerdo 7
3.7%
Disagree 7
3.7%
Strongly disagree 2
1.1%
Ni de acuerdo ni en desacuerdo 1
0.5%
(Missing) 21
11.1%

Length

2022-07-04T20:14:48.495620 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:48.775137 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 138
49.6%
strongly 42
15.1%
de 21
7.6%
acuerdo 21
7.6%
disagree 21
7.6%
neither 12
4.3%
or 12
4.3%
totalmente 7
2.5%
ni 2
0.7%
en 1
0.4%

Most occurring characters

Value Count Frequency (%)
e 401
23.8%
r 247
14.6%
g 201
11.9%
110
6.5%
a 102
6.0%
A 86
5.1%
o 83
4.9%
t 68
4.0%
n 51
3.0%
l 49
2.9%
Other values (12) 289
17.1%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1409
83.5%
Uppercase Letter 168
10.0%
Space Separator 110
6.5%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 401
28.5%
r 247
17.5%
g 201
14.3%
a 102
7.2%
o 83
5.9%
t 68
4.8%
n 51
3.6%
l 49
3.5%
d 45
3.2%
y 42
3.0%
Other values (6) 120
8.5%
Uppercase Letter
Value Count Frequency (%)
A 86
51.2%
S 42
25.0%
D 20
11.9%
N 13
7.7%
T 7
4.2%
Space Separator
Value Count Frequency (%)
110
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1577
93.5%
Common 110
6.5%

Most frequent character per script

Latin
Value Count Frequency (%)
e 401
25.4%
r 247
15.7%
g 201
12.7%
a 102
6.5%
A 86
5.5%
o 83
5.3%
t 68
4.3%
n 51
3.2%
l 49
3.1%
d 45
2.9%
Other values (11) 244
15.5%
Common
Value Count Frequency (%)
110
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1687
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 401
23.8%
r 247
14.6%
g 201
11.9%
110
6.5%
a 102
6.0%
A 86
5.1%
o 83
4.9%
t 68
4.0%
n 51
3.0%
l 49
2.9%
Other values (12) 289
17.1%

UX02[4]
Categorical

HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with the following statements. [I found the chatbot useful to get to know other students ]

Distinct 8
Distinct (%) 4.8%
Missing 21
Missing (%) 11.1%
Memory size 1.6 KiB
Agree
49
Strongly agree
32
Disagree
32
Neither agree or disagree
20
Totalmente de acuerdo
17
Other values (3)
18

Length

Max length 30
Median length 21
Mean length 12.52380952
Min length 5

Characters and Unicode

Total characters 2104
Distinct characters 22
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 1 ?
Unique (%) 0.6%

Sample

1st row Totalmente de acuerdo
2nd row Totalmente de acuerdo
3rd row Totalmente de acuerdo
4th row Ni de acuerdo ni en desacuerdo
5th row Totalmente de acuerdo

Common Values

Value Count Frequency (%)
Agree 49
25.9%
Strongly agree 32
16.9%
Disagree 32
16.9%
Neither agree or disagree 20
10.6%
Totalmente de acuerdo 17
9.0%
Strongly disagree 14
7.4%
De acuerdo 3
1.6%
Ni de acuerdo ni en desacuerdo 1
0.5%
(Missing) 21
11.1%

Length

2022-07-04T20:14:49.070868 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:49.368315 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 101
32.0%
disagree 66
20.9%
strongly 46
14.6%
de 21
6.6%
acuerdo 21
6.6%
neither 20
6.3%
or 20
6.3%
totalmente 17
5.4%
ni 2
0.6%
en 1
0.3%

Most occurring characters

Value Count Frequency (%)
e 453
21.5%
r 275
13.1%
g 213
10.1%
a 157
7.5%
148
7.0%
o 105
5.0%
t 100
4.8%
i 88
4.2%
d 75
3.6%
s 67
3.2%
Other values (12) 423
20.1%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1788
85.0%
Uppercase Letter 168
8.0%
Space Separator 148
7.0%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 453
25.3%
r 275
15.4%
g 213
11.9%
a 157
8.8%
o 105
5.9%
t 100
5.6%
i 88
4.9%
d 75
4.2%
s 67
3.7%
n 65
3.6%
Other values (6) 190
10.6%
Uppercase Letter
Value Count Frequency (%)
A 49
29.2%
S 46
27.4%
D 35
20.8%
N 21
12.5%
T 17
10.1%
Space Separator
Value Count Frequency (%)
148
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1956
93.0%
Common 148
7.0%

Most frequent character per script

Latin
Value Count Frequency (%)
e 453
23.2%
r 275
14.1%
g 213
10.9%
a 157
8.0%
o 105
5.4%
t 100
5.1%
i 88
4.5%
d 75
3.8%
s 67
3.4%
n 65
3.3%
Other values (11) 358
18.3%
Common
Value Count Frequency (%)
148
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 2104
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 453
21.5%
r 275
13.1%
g 213
10.1%
a 157
7.5%
148
7.0%
o 105
5.0%
t 100
4.8%
i 88
4.2%
d 75
3.6%
s 67
3.2%
Other values (12) 423
20.1%

UX02[5]
Categorical

HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with the following statements. [I found the chatbot useful to make me feel part of a community]

Distinct 8
Distinct (%) 4.8%
Missing 21
Missing (%) 11.1%
Memory size 1.6 KiB
Agree
59
Strongly agree
33
Disagree
26
Neither agree or disagree
24
Totalmente de acuerdo
10
Other values (3)
16

Length

Max length 30
Median length 25
Mean length 12.08333333
Min length 5

Characters and Unicode

Total characters 2030
Distinct characters 22
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Totalmente de acuerdo
2nd row Totalmente de acuerdo
3rd row De acuerdo
4th row Ni de acuerdo ni en desacuerdo
5th row De acuerdo

Common Values

Value Count Frequency (%)
Agree 59
31.2%
Strongly agree 33
17.5%
Disagree 26
13.8%
Neither agree or disagree 24
12.7%
Totalmente de acuerdo 10
5.3%
De acuerdo 8
4.2%
Strongly disagree 5
2.6%
Ni de acuerdo ni en desacuerdo 3
1.6%
(Missing) 21
11.1%

Length

2022-07-04T20:14:49.674152 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:49.970107 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 116
36.1%
disagree 55
17.1%
strongly 38
11.8%
neither 24
7.5%
or 24
7.5%
de 21
6.5%
acuerdo 21
6.5%
totalmente 10
3.1%
ni 6
1.9%
en 3
0.9%

Most occurring characters

Value Count Frequency (%)
e 461
22.7%
r 281
13.8%
g 209
10.3%
153
7.5%
a 146
7.2%
o 96
4.7%
i 85
4.2%
t 82
4.0%
d 69
3.4%
A 59
2.9%
Other values (12) 389
19.2%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1709
84.2%
Uppercase Letter 168
8.3%
Space Separator 153
7.5%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 461
27.0%
r 281
16.4%
g 209
12.2%
a 146
8.5%
o 96
5.6%
i 85
5.0%
t 82
4.8%
d 69
4.0%
s 58
3.4%
n 54
3.2%
Other values (6) 168
9.8%
Uppercase Letter
Value Count Frequency (%)
A 59
35.1%
S 38
22.6%
D 34
20.2%
N 27
16.1%
T 10
6.0%
Space Separator
Value Count Frequency (%)
153
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1877
92.5%
Common 153
7.5%

Most frequent character per script

Latin
Value Count Frequency (%)
e 461
24.6%
r 281
15.0%
g 209
11.1%
a 146
7.8%
o 96
5.1%
i 85
4.5%
t 82
4.4%
d 69
3.7%
A 59
3.1%
s 58
3.1%
Other values (11) 331
17.6%
Common
Value Count Frequency (%)
153
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 2030
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 461
22.7%
r 281
13.8%
g 209
10.3%
153
7.5%
a 146
7.2%
o 96
4.7%
i 85
4.2%
t 82
4.0%
d 69
3.4%
A 59
2.9%
Other values (12) 389
19.2%

UX02[6]
Categorical

HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with the following statements. [I felt comfortable using the chatbot to ask questions]

Distinct 8
Distinct (%) 4.8%
Missing 21
Missing (%) 11.1%
Memory size 1.6 KiB
Agree
78
Strongly agree
36
Neither agree or disagree
19
De acuerdo
12
Disagree
9
Other values (3)
14

Length

Max length 30
Median length 25
Mean length 11.0297619
Min length 5

Characters and Unicode

Total characters 1853
Distinct characters 22
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Totalmente de acuerdo
2nd row Totalmente de acuerdo
3rd row De acuerdo
4th row De acuerdo
5th row De acuerdo

Common Values

Value Count Frequency (%)
Agree 78
41.3%
Strongly agree 36
19.0%
Neither agree or disagree 19
10.1%
De acuerdo 12
6.3%
Disagree 9
4.8%
Totalmente de acuerdo 7
3.7%
Strongly disagree 5
2.6%
Ni de acuerdo ni en desacuerdo 2
1.1%
(Missing) 21
11.1%

Length

2022-07-04T20:14:50.268658 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:50.552398 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 133
44.0%
strongly 41
13.6%
disagree 33
10.9%
de 21
7.0%
acuerdo 21
7.0%
neither 19
6.3%
or 19
6.3%
totalmente 7
2.3%
ni 4
1.3%
en 2
0.7%

Most occurring characters

Value Count Frequency (%)
e 432
23.3%
r 268
14.5%
g 207
11.2%
134
7.2%
a 118
6.4%
o 90
4.9%
A 78
4.2%
t 74
4.0%
d 58
3.1%
i 56
3.0%
Other values (12) 338
18.2%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1551
83.7%
Uppercase Letter 168
9.1%
Space Separator 134
7.2%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 432
27.9%
r 268
17.3%
g 207
13.3%
a 118
7.6%
o 90
5.8%
t 74
4.8%
d 58
3.7%
i 56
3.6%
n 52
3.4%
l 48
3.1%
Other values (6) 148
9.5%
Uppercase Letter
Value Count Frequency (%)
A 78
46.4%
S 41
24.4%
N 21
12.5%
D 21
12.5%
T 7
4.2%
Space Separator
Value Count Frequency (%)
134
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1719
92.8%
Common 134
7.2%

Most frequent character per script

Latin
Value Count Frequency (%)
e 432
25.1%
r 268
15.6%
g 207
12.0%
a 118
6.9%
o 90
5.2%
A 78
4.5%
t 74
4.3%
d 58
3.4%
i 56
3.3%
n 52
3.0%
Other values (11) 286
16.6%
Common
Value Count Frequency (%)
134
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1853
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 432
23.3%
r 268
14.5%
g 207
11.2%
134
7.2%
a 118
6.4%
o 90
4.9%
A 78
4.2%
t 74
4.0%
d 58
3.1%
i 56
3.0%
Other values (12) 338
18.2%

UX02[7]
Categorical

HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with the following statements. [I felt comfortable using the chatbot to answer questions]

Distinct 8
Distinct (%) 4.8%
Missing 21
Missing (%) 11.1%
Memory size 1.6 KiB
Agree
71
Strongly agree
53
Neither agree or disagree
13
Totalmente de acuerdo
11
De acuerdo
8
Other values (3)
12

Length

Max length 30
Median length 25
Mean length 11.36309524
Min length 5

Characters and Unicode

Total characters 1909
Distinct characters 22
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Totalmente de acuerdo
2nd row Totalmente de acuerdo
3rd row De acuerdo
4th row De acuerdo
5th row De acuerdo

Common Values

Value Count Frequency (%)
Agree 71
37.6%
Strongly agree 53
28.0%
Neither agree or disagree 13
6.9%
Totalmente de acuerdo 11
5.8%
De acuerdo 8
4.2%
Disagree 6
3.2%
Strongly disagree 4
2.1%
Ni de acuerdo ni en desacuerdo 2
1.1%
(Missing) 21
11.1%

Length

2022-07-04T20:14:50.853450 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:51.143765 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 137
45.1%
strongly 57
18.8%
disagree 23
7.6%
de 21
6.9%
acuerdo 21
6.9%
neither 13
4.3%
or 13
4.3%
totalmente 11
3.6%
ni 4
1.3%
en 2
0.7%

Most occurring characters

Value Count Frequency (%)
e 416
21.8%
r 266
13.9%
g 217
11.4%
136
7.1%
a 123
6.4%
o 104
5.4%
t 92
4.8%
n 72
3.8%
A 71
3.7%
l 68
3.6%
Other values (12) 344
18.0%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1605
84.1%
Uppercase Letter 168
8.8%
Space Separator 136
7.1%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 416
25.9%
r 266
16.6%
g 217
13.5%
a 123
7.7%
o 104
6.5%
t 92
5.7%
n 72
4.5%
l 68
4.2%
y 57
3.6%
d 55
3.4%
Other values (6) 135
8.4%
Uppercase Letter
Value Count Frequency (%)
A 71
42.3%
S 57
33.9%
N 15
8.9%
D 14
8.3%
T 11
6.5%
Space Separator
Value Count Frequency (%)
136
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1773
92.9%
Common 136
7.1%

Most frequent character per script

Latin
Value Count Frequency (%)
e 416
23.5%
r 266
15.0%
g 217
12.2%
a 123
6.9%
o 104
5.9%
t 92
5.2%
n 72
4.1%
A 71
4.0%
l 68
3.8%
y 57
3.2%
Other values (11) 287
16.2%
Common
Value Count Frequency (%)
136
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1909
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 416
21.8%
r 266
13.9%
g 217
11.4%
136
7.1%
a 123
6.4%
o 104
5.4%
t 92
4.8%
n 72
3.8%
A 71
3.7%
l 68
3.6%
Other values (12) 344
18.0%

UX02[8]
Categorical

HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with the following statements. [I felt pleased to be able to provide an answer]

Distinct 8
Distinct (%) 4.8%
Missing 21
Missing (%) 11.1%
Memory size 1.6 KiB
Agree
64
Strongly agree
63
Neither agree or disagree
13
Totalmente de acuerdo
10
De acuerdo
8
Other values (3)
10

Length

Max length 30
Median length 25
Mean length 11.8452381
Min length 5

Characters and Unicode

Total characters 1990
Distinct characters 22
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Totalmente de acuerdo
2nd row Totalmente de acuerdo
3rd row Totalmente de acuerdo
4th row De acuerdo
5th row De acuerdo

Common Values

Value Count Frequency (%)
Agree 64
33.9%
Strongly agree 63
33.3%
Neither agree or disagree 13
6.9%
Totalmente de acuerdo 10
5.3%
De acuerdo 8
4.2%
Disagree 4
2.1%
Ni de acuerdo ni en desacuerdo 3
1.6%
Strongly disagree 3
1.6%
(Missing) 21
11.1%

Length

2022-07-04T20:14:51.438259 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:51.729834 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 140
44.3%
strongly 66
20.9%
de 21
6.6%
acuerdo 21
6.6%
disagree 20
6.3%
neither 13
4.1%
or 13
4.1%
totalmente 10
3.2%
ni 6
1.9%
en 3
0.9%

Most occurring characters

Value Count Frequency (%)
e 417
21.0%
r 276
13.9%
g 226
11.4%
148
7.4%
a 130
6.5%
o 113
5.7%
t 99
5.0%
n 82
4.1%
l 76
3.8%
S 66
3.3%
Other values (12) 357
17.9%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1674
84.1%
Uppercase Letter 168
8.4%
Space Separator 148
7.4%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 417
24.9%
r 276
16.5%
g 226
13.5%
a 130
7.8%
o 113
6.8%
t 99
5.9%
n 82
4.9%
l 76
4.5%
y 66
3.9%
d 56
3.3%
Other values (6) 133
7.9%
Uppercase Letter
Value Count Frequency (%)
S 66
39.3%
A 64
38.1%
N 16
9.5%
D 12
7.1%
T 10
6.0%
Space Separator
Value Count Frequency (%)
148
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1842
92.6%
Common 148
7.4%

Most frequent character per script

Latin
Value Count Frequency (%)
e 417
22.6%
r 276
15.0%
g 226
12.3%
a 130
7.1%
o 113
6.1%
t 99
5.4%
n 82
4.5%
l 76
4.1%
S 66
3.6%
y 66
3.6%
Other values (11) 291
15.8%
Common
Value Count Frequency (%)
148
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1990
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 417
21.0%
r 276
13.9%
g 226
11.4%
148
7.4%
a 130
6.5%
o 113
5.7%
t 99
5.0%
n 82
4.1%
l 76
3.8%
S 66
3.3%
Other values (12) 357
17.9%

UX02[9]
Categorical

HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with the following statements. [I felt pleased to get answers to my questions]

Distinct 8
Distinct (%) 4.8%
Missing 21
Missing (%) 11.1%
Memory size 1.6 KiB
Agree
66
Strongly agree
58
Neither agree or disagree
17
De acuerdo
13
Totalmente de acuerdo
6
Other values (3)
8

Length

Max length 30
Median length 25
Mean length 11.60119048
Min length 5

Characters and Unicode

Total characters 1949
Distinct characters 22
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row De acuerdo
2nd row De acuerdo
3rd row Ni de acuerdo ni en desacuerdo
4th row De acuerdo
5th row De acuerdo

Common Values

Value Count Frequency (%)
Agree 66
34.9%
Strongly agree 58
30.7%
Neither agree or disagree 17
9.0%
De acuerdo 13
6.9%
Totalmente de acuerdo 6
3.2%
Disagree 4
2.1%
Ni de acuerdo ni en desacuerdo 2
1.1%
Strongly disagree 2
1.1%
(Missing) 21
11.1%

Length

2022-07-04T20:14:52.023570 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:52.315205 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 141
44.9%
strongly 60
19.1%
disagree 23
7.3%
de 21
6.7%
acuerdo 21
6.7%
neither 17
5.4%
or 17
5.4%
totalmente 6
1.9%
ni 4
1.3%
en 2
0.6%

Most occurring characters

Value Count Frequency (%)
e 422
21.7%
r 281
14.4%
g 224
11.5%
146
7.5%
a 127
6.5%
o 106
5.4%
t 89
4.6%
n 70
3.6%
A 66
3.4%
l 66
3.4%
Other values (12) 352
18.1%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1635
83.9%
Uppercase Letter 168
8.6%
Space Separator 146
7.5%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 422
25.8%
r 281
17.2%
g 224
13.7%
a 127
7.8%
o 106
6.5%
t 89
5.4%
n 70
4.3%
l 66
4.0%
y 60
3.7%
d 52
3.2%
Other values (6) 138
8.4%
Uppercase Letter
Value Count Frequency (%)
A 66
39.3%
S 60
35.7%
N 19
11.3%
D 17
10.1%
T 6
3.6%
Space Separator
Value Count Frequency (%)
146
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1803
92.5%
Common 146
7.5%

Most frequent character per script

Latin
Value Count Frequency (%)
e 422
23.4%
r 281
15.6%
g 224
12.4%
a 127
7.0%
o 106
5.9%
t 89
4.9%
n 70
3.9%
A 66
3.7%
l 66
3.7%
y 60
3.3%
Other values (11) 292
16.2%
Common
Value Count Frequency (%)
146
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1949
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 422
21.7%
r 281
14.4%
g 224
11.5%
146
7.5%
a 127
6.5%
o 106
5.4%
t 89
4.6%
n 70
3.6%
A 66
3.4%
l 66
3.4%
Other values (12) 352
18.1%

UX02[10]
Categorical

HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with the following statements. [The chatbot had an appealing tone of voice]

Distinct 9
Distinct (%) 5.4%
Missing 21
Missing (%) 11.1%
Memory size 1.6 KiB
Neither agree or disagree
73
Agree
39
Strongly agree
20
Ni de acuerdo ni en desacuerdo
13
Disagree
11
Other values (4)
12

Length

Max length 30
Median length 25
Mean length 17.76190476
Min length 5

Characters and Unicode

Total characters 2984
Distinct characters 23
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 1 ?
Unique (%) 0.6%

Sample

1st row En desacuerdo
2nd row Ni de acuerdo ni en desacuerdo
3rd row Ni de acuerdo ni en desacuerdo
4th row Ni de acuerdo ni en desacuerdo
5th row Ni de acuerdo ni en desacuerdo

Common Values

Value Count Frequency (%)
Neither agree or disagree 73
38.6%
Agree 39
20.6%
Strongly agree 20
10.6%
Ni de acuerdo ni en desacuerdo 13
6.9%
Disagree 11
5.8%
Totalmente de acuerdo 5
2.6%
Strongly disagree 4
2.1%
De acuerdo 2
1.1%
En desacuerdo 1
0.5%
(Missing) 21
11.1%

Length

2022-07-04T20:14:52.618773 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:52.919773 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 132
27.0%
disagree 88
18.0%
neither 73
14.9%
or 73
14.9%
ni 26
5.3%
strongly 24
4.9%
de 20
4.1%
acuerdo 20
4.1%
en 14
2.9%
desacuerdo 14
2.9%

Most occurring characters

Value Count Frequency (%)
e 677
22.7%
r 424
14.2%
321
10.8%
g 244
8.2%
a 220
7.4%
i 187
6.3%
d 143
4.8%
o 136
4.6%
t 107
3.6%
s 102
3.4%
Other values (13) 423
14.2%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 2495
83.6%
Space Separator 321
10.8%
Uppercase Letter 168
5.6%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 677
27.1%
r 424
17.0%
g 244
9.8%
a 220
8.8%
i 187
7.5%
d 143
5.7%
o 136
5.5%
t 107
4.3%
s 102
4.1%
h 73
2.9%
Other values (6) 182
7.3%
Uppercase Letter
Value Count Frequency (%)
N 86
51.2%
A 39
23.2%
S 24
14.3%
D 13
7.7%
T 5
3.0%
E 1
0.6%
Space Separator
Value Count Frequency (%)
321
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 2663
89.2%
Common 321
10.8%

Most frequent character per script

Latin
Value Count Frequency (%)
e 677
25.4%
r 424
15.9%
g 244
9.2%
a 220
8.3%
i 187
7.0%
d 143
5.4%
o 136
5.1%
t 107
4.0%
s 102
3.8%
N 86
3.2%
Other values (12) 337
12.7%
Common
Value Count Frequency (%)
321
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 2984
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 677
22.7%
r 424
14.2%
321
10.8%
g 244
8.2%
a 220
7.4%
i 187
6.3%
d 143
4.8%
o 136
4.6%
t 107
3.6%
s 102
3.4%
Other values (13) 423
14.2%

UX02[11]
Categorical

HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with the following statements. [I found the chatbot trustworthy ]

Distinct 9
Distinct (%) 5.4%
Missing 21
Missing (%) 11.1%
Memory size 1.6 KiB
Agree
57
Neither agree or disagree
43
Strongly agree
31
De acuerdo
12
Disagree
12
Other values (4)
13

Length

Max length 30
Median length 25
Mean length 13.71428571
Min length 5

Characters and Unicode

Total characters 2304
Distinct characters 23
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 1 ?
Unique (%) 0.6%

Sample

1st row Ni de acuerdo ni en desacuerdo
2nd row De acuerdo
3rd row De acuerdo
4th row De acuerdo
5th row De acuerdo

Common Values

Value Count Frequency (%)
Agree 57
30.2%
Neither agree or disagree 43
22.8%
Strongly agree 31
16.4%
De acuerdo 12
6.3%
Disagree 12
6.3%
Ni de acuerdo ni en desacuerdo 5
2.6%
Strongly disagree 4
2.1%
Totalmente de acuerdo 3
1.6%
En desacuerdo 1
0.5%
(Missing) 21
11.1%

Length

2022-07-04T20:14:53.227558 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:53.514537 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 131
34.8%
disagree 59
15.7%
neither 43
11.4%
or 43
11.4%
strongly 35
9.3%
de 20
5.3%
acuerdo 20
5.3%
ni 10
2.7%
en 6
1.6%
desacuerdo 6
1.6%

Most occurring characters

Value Count Frequency (%)
e 529
23.0%
r 337
14.6%
g 225
9.8%
208
9.0%
a 162
7.0%
i 112
4.9%
o 107
4.6%
d 87
3.8%
t 84
3.6%
s 65
2.8%
Other values (13) 388
16.8%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1928
83.7%
Space Separator 208
9.0%
Uppercase Letter 168
7.3%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 529
27.4%
r 337
17.5%
g 225
11.7%
a 162
8.4%
i 112
5.8%
o 107
5.5%
d 87
4.5%
t 84
4.4%
s 65
3.4%
n 49
2.5%
Other values (6) 171
8.9%
Uppercase Letter
Value Count Frequency (%)
A 57
33.9%
N 48
28.6%
S 35
20.8%
D 24
14.3%
T 3
1.8%
E 1
0.6%
Space Separator
Value Count Frequency (%)
208
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 2096
91.0%
Common 208
9.0%

Most frequent character per script

Latin
Value Count Frequency (%)
e 529
25.2%
r 337
16.1%
g 225
10.7%
a 162
7.7%
i 112
5.3%
o 107
5.1%
d 87
4.2%
t 84
4.0%
s 65
3.1%
A 57
2.7%
Other values (12) 331
15.8%
Common
Value Count Frequency (%)
208
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 2304
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 529
23.0%
r 337
14.6%
g 225
9.8%
208
9.0%
a 162
7.0%
i 112
4.9%
o 107
4.6%
d 87
3.8%
t 84
3.6%
s 65
2.8%
Other values (13) 388
16.8%

UX02[12]
Categorical

HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with the following statements. [Using the chatbot was rewarding]

Distinct 9
Distinct (%) 5.4%
Missing 21
Missing (%) 11.1%
Memory size 1.6 KiB
Agree
56
Neither agree or disagree
54
Disagree
16
Strongly agree
16
De acuerdo
11
Other values (4)
15

Length

Max length 30
Median length 25
Mean length 14.26785714
Min length 5

Characters and Unicode

Total characters 2397
Distinct characters 23
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 1 ?
Unique (%) 0.6%

Sample

1st row De acuerdo
2nd row De acuerdo
3rd row De acuerdo
4th row De acuerdo
5th row De acuerdo

Common Values

Value Count Frequency (%)
Agree 56
29.6%
Neither agree or disagree 54
28.6%
Disagree 16
8.5%
Strongly agree 16
8.5%
De acuerdo 11
5.8%
Totalmente de acuerdo 7
3.7%
Strongly disagree 5
2.6%
Ni de acuerdo ni en desacuerdo 2
1.1%
En desacuerdo 1
0.5%
(Missing) 21
11.1%

Length

2022-07-04T20:14:53.818409 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:54.349607 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 126
32.6%
disagree 75
19.4%
neither 54
14.0%
or 54
14.0%
strongly 21
5.4%
de 20
5.2%
acuerdo 20
5.2%
totalmente 7
1.8%
ni 4
1.0%
en 3
0.8%

Most occurring characters

Value Count Frequency (%)
e 572
23.9%
r 353
14.7%
g 222
9.3%
219
9.1%
a 175
7.3%
i 133
5.5%
o 105
4.4%
d 94
3.9%
t 89
3.7%
s 78
3.3%
Other values (13) 357
14.9%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 2010
83.9%
Space Separator 219
9.1%
Uppercase Letter 168
7.0%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 572
28.5%
r 353
17.6%
g 222
11.0%
a 175
8.7%
i 133
6.6%
o 105
5.2%
d 94
4.7%
t 89
4.4%
s 78
3.9%
h 54
2.7%
Other values (6) 135
6.7%
Uppercase Letter
Value Count Frequency (%)
A 56
33.3%
N 56
33.3%
D 27
16.1%
S 21
12.5%
T 7
4.2%
E 1
0.6%
Space Separator
Value Count Frequency (%)
219
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 2178
90.9%
Common 219
9.1%

Most frequent character per script

Latin
Value Count Frequency (%)
e 572
26.3%
r 353
16.2%
g 222
10.2%
a 175
8.0%
i 133
6.1%
o 105
4.8%
d 94
4.3%
t 89
4.1%
s 78
3.6%
A 56
2.6%
Other values (12) 301
13.8%
Common
Value Count Frequency (%)
219
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 2397
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 572
23.9%
r 353
14.7%
g 222
9.3%
219
9.1%
a 175
7.3%
i 133
5.5%
o 105
4.4%
d 94
3.9%
t 89
3.7%
s 78
3.3%
Other values (13) 357
14.9%

UX02[13]
Categorical

HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with the following statements. [Using the chatbot was fun]

Distinct 8
Distinct (%) 4.8%
Missing 21
Missing (%) 11.1%
Memory size 1.6 KiB
Agree
66
Neither agree or disagree
35
Strongly agree
28
Totalmente de acuerdo
12
Disagree
12
Other values (3)
15

Length

Max length 30
Median length 25
Mean length 12.83928571
Min length 5

Characters and Unicode

Total characters 2157
Distinct characters 22
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 1 ?
Unique (%) 0.6%

Sample

1st row Totalmente de acuerdo
2nd row De acuerdo
3rd row De acuerdo
4th row De acuerdo
5th row De acuerdo

Common Values

Value Count Frequency (%)
Agree 66
34.9%
Neither agree or disagree 35
18.5%
Strongly agree 28
14.8%
Totalmente de acuerdo 12
6.3%
Disagree 12
6.3%
De acuerdo 8
4.2%
Strongly disagree 6
3.2%
Ni de acuerdo ni en desacuerdo 1
0.5%
(Missing) 21
11.1%

Length

2022-07-04T20:14:54.661032 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:54.955040 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 129
37.5%
disagree 53
15.4%
neither 35
10.2%
or 35
10.2%
strongly 34
9.9%
de 21
6.1%
acuerdo 21
6.1%
totalmente 12
3.5%
ni 2
0.6%
en 1
0.3%

Most occurring characters

Value Count Frequency (%)
e 503
23.3%
r 308
14.3%
g 216
10.0%
176
8.2%
a 150
7.0%
o 103
4.8%
t 93
4.3%
i 90
4.2%
d 77
3.6%
A 66
3.1%
Other values (12) 375
17.4%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1813
84.1%
Space Separator 176
8.2%
Uppercase Letter 168
7.8%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 503
27.7%
r 308
17.0%
g 216
11.9%
a 150
8.3%
o 103
5.7%
t 93
5.1%
i 90
5.0%
d 77
4.2%
s 54
3.0%
n 48
2.6%
Other values (6) 171
9.4%
Uppercase Letter
Value Count Frequency (%)
A 66
39.3%
N 36
21.4%
S 34
20.2%
D 20
11.9%
T 12
7.1%
Space Separator
Value Count Frequency (%)
176
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1981
91.8%
Common 176
8.2%

Most frequent character per script

Latin
Value Count Frequency (%)
e 503
25.4%
r 308
15.5%
g 216
10.9%
a 150
7.6%
o 103
5.2%
t 93
4.7%
i 90
4.5%
d 77
3.9%
A 66
3.3%
s 54
2.7%
Other values (11) 321
16.2%
Common
Value Count Frequency (%)
176
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 2157
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 503
23.3%
r 308
14.3%
g 216
10.0%
176
8.2%
a 150
7.0%
o 103
4.8%
t 93
4.3%
i 90
4.2%
d 77
3.6%
A 66
3.1%
Other values (12) 375
17.4%

UX02[14]
Categorical

HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with the following statements. [I was interested in the experience of chatbot ]

Distinct 7
Distinct (%) 4.2%
Missing 21
Missing (%) 11.1%
Memory size 1.6 KiB
Agree
76
Strongly agree
37
Neither agree or disagree
23
Totalmente de acuerdo
13
Disagree
9
Other values (2)
10

Length

Max length 25
Median length 21
Mean length 11.5
Min length 5

Characters and Unicode

Total characters 1932
Distinct characters 22
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row De acuerdo
2nd row Totalmente de acuerdo
3rd row De acuerdo
4th row De acuerdo
5th row De acuerdo

Common Values

Value Count Frequency (%)
Agree 76
40.2%
Strongly agree 37
19.6%
Neither agree or disagree 23
12.2%
Totalmente de acuerdo 13
6.9%
Disagree 9
4.8%
De acuerdo 8
4.2%
Strongly disagree 2
1.1%
(Missing) 21
11.1%

Length

2022-07-04T20:14:55.263437 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:55.566525 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 136
43.9%
strongly 39
12.6%
disagree 34
11.0%
neither 23
7.4%
or 23
7.4%
de 21
6.8%
acuerdo 21
6.8%
totalmente 13
4.2%

Most occurring characters

Value Count Frequency (%)
e 454
23.5%
r 276
14.3%
g 209
10.8%
142
7.3%
a 128
6.6%
o 96
5.0%
t 88
4.6%
A 76
3.9%
d 59
3.1%
i 57
3.0%
Other values (12) 347
18.0%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1622
84.0%
Uppercase Letter 168
8.7%
Space Separator 142
7.3%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 454
28.0%
r 276
17.0%
g 209
12.9%
a 128
7.9%
o 96
5.9%
t 88
5.4%
d 59
3.6%
i 57
3.5%
l 52
3.2%
n 52
3.2%
Other values (6) 151
9.3%
Uppercase Letter
Value Count Frequency (%)
A 76
45.2%
S 39
23.2%
N 23
13.7%
D 17
10.1%
T 13
7.7%
Space Separator
Value Count Frequency (%)
142
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1790
92.7%
Common 142
7.3%

Most frequent character per script

Latin
Value Count Frequency (%)
e 454
25.4%
r 276
15.4%
g 209
11.7%
a 128
7.2%
o 96
5.4%
t 88
4.9%
A 76
4.2%
d 59
3.3%
i 57
3.2%
l 52
2.9%
Other values (11) 295
16.5%
Common
Value Count Frequency (%)
142
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1932
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 454
23.5%
r 276
14.3%
g 209
10.8%
142
7.3%
a 128
6.6%
o 96
5.0%
t 88
4.6%
A 76
3.9%
d 59
3.1%
i 57
3.0%
Other values (12) 347
18.0%

UX02[15]
Categorical

HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with the following statements. [I would keep using the chatbot in my everyday life]

Distinct 8
Distinct (%) 4.8%
Missing 21
Missing (%) 11.1%
Memory size 1.6 KiB
Agree
55
Neither agree or disagree
34
Disagree
33
Strongly agree
16
De acuerdo
10
Other values (3)
20

Length

Max length 30
Median length 25
Mean length 12.75
Min length 5

Characters and Unicode

Total characters 2142
Distinct characters 22
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row De acuerdo
2nd row De acuerdo
3rd row Ni de acuerdo ni en desacuerdo
4th row Ni de acuerdo ni en desacuerdo
5th row De acuerdo

Common Values

Value Count Frequency (%)
Agree 55
29.1%
Neither agree or disagree 34
18.0%
Disagree 33
17.5%
Strongly agree 16
8.5%
De acuerdo 10
5.3%
Strongly disagree 9
4.8%
Totalmente de acuerdo 6
3.2%
Ni de acuerdo ni en desacuerdo 5
2.6%
(Missing) 21
11.1%

Length

2022-07-04T20:14:55.841970 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:56.121058 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
agree 105
30.7%
disagree 76
22.2%
neither 34
9.9%
or 34
9.9%
strongly 25
7.3%
de 21
6.1%
acuerdo 21
6.1%
ni 10
2.9%
totalmente 6
1.8%
en 5
1.5%

Most occurring characters

Value Count Frequency (%)
e 499
23.3%
r 300
14.0%
g 206
9.6%
174
8.1%
a 158
7.4%
i 120
5.6%
o 91
4.2%
d 85
4.0%
s 81
3.8%
t 71
3.3%
Other values (12) 357
16.7%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1800
84.0%
Space Separator 174
8.1%
Uppercase Letter 168
7.8%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 499
27.7%
r 300
16.7%
g 206
11.4%
a 158
8.8%
i 120
6.7%
o 91
5.1%
d 85
4.7%
s 81
4.5%
t 71
3.9%
n 41
2.3%
Other values (6) 148
8.2%
Uppercase Letter
Value Count Frequency (%)
A 55
32.7%
D 43
25.6%
N 39
23.2%
S 25
14.9%
T 6
3.6%
Space Separator
Value Count Frequency (%)
174
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1968
91.9%
Common 174
8.1%

Most frequent character per script

Latin
Value Count Frequency (%)
e 499
25.4%
r 300
15.2%
g 206
10.5%
a 158
8.0%
i 120
6.1%
o 91
4.6%
d 85
4.3%
s 81
4.1%
t 71
3.6%
A 55
2.8%
Other values (11) 302
15.3%
Common
Value Count Frequency (%)
174
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 2142
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 499
23.3%
r 300
14.0%
g 206
9.6%
174
8.1%
a 158
7.4%
i 120
5.6%
o 91
4.2%
d 85
4.0%
s 81
3.8%
t 71
3.3%
Other values (12) 357
16.7%

UX02[16]
Categorical

HIGH CORRELATION
MISSING

Please indicate whether you agree or disagree with the following statements. [I use other chatbots in my everyday life]

Distinct 9
Distinct (%) 5.4%
Missing 21
Missing (%) 11.1%
Memory size 1.6 KiB
Strongly disagree
53
Disagree
49
Neither agree or disagree
27
Agree
13
Totalmente en desacuerdo
11
Other values (4)
15

Length

Max length 30
Median length 25
Mean length 14.97619048
Min length 5

Characters and Unicode

Total characters 2516
Distinct characters 23
Distinct categories 3 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row Totalmente en desacuerdo
2nd row Totalmente en desacuerdo
3rd row En desacuerdo
4th row Totalmente en desacuerdo
5th row Ni de acuerdo ni en desacuerdo

Common Values

Value Count Frequency (%)
Strongly disagree 53
28.0%
Disagree 49
25.9%
Neither agree or disagree 27
14.3%
Agree 13
6.9%
Totalmente en desacuerdo 11
5.8%
De acuerdo 5
2.6%
Strongly agree 5
2.6%
En desacuerdo 3
1.6%
Ni de acuerdo ni en desacuerdo 2
1.1%
(Missing) 21
11.1%

Length

2022-07-04T20:14:56.423656 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:56.710500 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
disagree 129
37.2%
strongly 58
16.7%
agree 45
13.0%
neither 27
7.8%
or 27
7.8%
en 16
4.6%
desacuerdo 16
4.6%
totalmente 11
3.2%
de 7
2.0%
acuerdo 7
2.0%

Most occurring characters

Value Count Frequency (%)
e 483
19.2%
r 309
12.3%
g 232
9.2%
a 195
7.8%
179
7.1%
i 160
6.4%
s 145
5.8%
d 121
4.8%
o 119
4.7%
t 107
4.3%
Other values (13) 466
18.5%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 2169
86.2%
Space Separator 179
7.1%
Uppercase Letter 168
6.7%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 483
22.3%
r 309
14.2%
g 232
10.7%
a 195
9.0%
i 160
7.4%
s 145
6.7%
d 121
5.6%
o 119
5.5%
t 107
4.9%
n 87
4.0%
Other values (6) 211
9.7%
Uppercase Letter
Value Count Frequency (%)
S 58
34.5%
D 54
32.1%
N 29
17.3%
A 13
7.7%
T 11
6.5%
E 3
1.8%
Space Separator
Value Count Frequency (%)
179
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 2337
92.9%
Common 179
7.1%

Most frequent character per script

Latin
Value Count Frequency (%)
e 483
20.7%
r 309
13.2%
g 232
9.9%
a 195
8.3%
i 160
6.8%
s 145
6.2%
d 121
5.2%
o 119
5.1%
t 107
4.6%
n 87
3.7%
Other values (12) 379
16.2%
Common
Value Count Frequency (%)
179
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 2516
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 483
19.2%
r 309
12.3%
g 232
9.2%
a 195
7.8%
179
7.1%
i 160
6.4%
s 145
5.8%
d 121
4.8%
o 119
4.7%
t 107
4.3%
Other values (13) 466
18.5%

F01
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING
UNIFORM

Thanks for completing the questionnaire! Please click on the "Submit" button to finalize your answers. If you have any other comments on the chatbot, we would be pleased to read them.

Distinct 75
Distinct (%) 100.0%
Missing 114
Missing (%) 60.3%
Memory size 1.6 KiB
Fue bueno para conocer a gente de la facu y de paso se sintió como un grupo/familia
1
No relevant comment
1
миний хувьд чат бот их хөгжилтэй сайхан байлаа баярлалаа. 1. Хариултууд дээр ганц таалагдлаа биш харин реакшин дарж болдог болговол гоё байна 2. Урт, их юм бичихээр хариулт маань явддаггүй байсан. Хариултын үгийн тоо хэмжээг нэмэгдүүлж өгвөл сайн байна. 3. Өөр асуулт, хариулт ирэхээр яг хийж байсан үйл ажиллагаа автоматаар цугцлагддаг нь таалагдаагүй. Тэгж цуцлагддагыг нь болиулж болдог бол сайнсан.
1
Асуулт асууж байх эсвэл асуултанд хариулж байх явцад шинэ хариулт эсвэл асуулт ирэхэд алдаа зааж байсан энэ нь гоё асуултанд маш уртаар хариулахад хүндрэлтэй байсан. Тэрнээс бусдаар маш өгөөжтэй байлаа
1
Асуулт асуухад үгийн тооны хязгаар байх шиг байна лээ. Тэрийг их болговол зүгээр санагдсан. Мөн хариулт авсан хүнтэйгээ эргэж чатладаг бол мөн стикер ашигладаг бол гоё.
1
Other values (70)
70

Length

Max length 911
Median length 202
Mean length 252.4533333
Min length 19

Characters and Unicode

Total characters 18934
Distinct characters 129
Distinct categories 11 ?
Distinct scripts 3 ?
Distinct blocks 5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 75 ?
Unique (%) 100.0%

Sample

1st row Fue bueno para conocer a gente de la facu y de paso se sintió como un grupo/familia
2nd row Me di cuenta de que en un punto surgieron las ganas de conocer a los demás participantes del experimento, por lo que comenzamos a conversar a través del comando para preguntas jajaja fue una linda experiencia y me alegra haber formado parte de ella
3rd row Lo único que malo que vi fue que a veces había dificultad a la hora de responder preguntas
4th row Creo que se debería de solucionar el problema que hay cuando se enciman mucho las preguntas y respuestas ya que hay un momento en el que cuando hay mucho uso del chatboot colapsa y ya no podes preguntar o responder porque ya no te permite, ya que dice que ya expiró
5th row Se buggeaba cuando usábamos muchos, si alguien preguntaba y no respondías la pregunta anterior a esa... ya no podías responder

Common Values

Value Count Frequency (%)
Fue bueno para conocer a gente de la facu y de paso se sintió como un grupo/familia 1
0.5%
No relevant comment 1
0.5%
миний хувьд чат бот их хөгжилтэй сайхан байлаа баярлалаа. 1. Хариултууд дээр ганц таалагдлаа биш харин реакшин дарж болдог болговол гоё байна 2. Урт, их юм бичихээр хариулт маань явддаггүй байсан. Хариултын үгийн тоо хэмжээг нэмэгдүүлж өгвөл сайн байна. 3. Өөр асуулт, хариулт ирэхээр яг хийж байсан үйл ажиллагаа автоматаар цугцлагддаг нь таалагдаагүй. Тэгж цуцлагддагыг нь болиулж болдог бол сайнсан. 1
0.5%
Асуулт асууж байх эсвэл асуултанд хариулж байх явцад шинэ хариулт эсвэл асуулт ирэхэд алдаа зааж байсан энэ нь гоё асуултанд маш уртаар хариулахад хүндрэлтэй байсан. Тэрнээс бусдаар маш өгөөжтэй байлаа 1
0.5%
Асуулт асуухад үгийн тооны хязгаар байх шиг байна лээ. Тэрийг их болговол зүгээр санагдсан. Мөн хариулт авсан хүнтэйгээ эргэж чатладаг бол мөн стикер ашигладаг бол гоё. 1
0.5%
Эможи дардаг болгомоор байна. Дээрээс нь асуултанд хариулж байхад өөр асуулт ирэхээр хариулж байсан хариулт цуцлагддаг. Үүн дээр анхаармаар байна. 1
0.5%
Thanks for providing the oppotunity to test the chatbot! One bug that I experienced quite some times, was that when I was typing out answers, the bot will interrupt with something and my answer would no longer be submitteable. It happened at least three times and was quite discouraging. Hopefully, the bot can learn not to intercept when one is taking the time to type out answers. Another thing was the timing for the notifications, sometimes it woudl spam me, other times keep very quiet. 1
0.5%
Sometimes I wished I could see other people's answers. Also, at first chatbot was fun, but when the novelty of it wore out, I didn't REALLY have a need for asking things. 1
0.5%
Got a lot of "there is a question i think that you can answer best!" but even if i tried to answer one of those that arrived 2 minutes before my answer, my answer would not get accepted. It was a bit annoying when youve put time into writing the answer. 1
0.5%
It was fun, although I didn't understand the overall idea of the test, so it made me confusing at times. Good luck on your work and thank you. 1
0.5%
Other values (65) 65
34.4%
(Missing) 114
60.3%

Length

2022-07-04T20:14:57.071854 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
Value Count Frequency (%)
the 87
2.7%
i 81
2.5%
to 61
1.9%
a 57
1.8%
it 47
1.5%
was 41
1.3%
and 32
1.0%
questions 31
1.0%
answer 26
0.8%
would 26
0.8%
Other values (1230) 2691
84.6%

Most occurring characters

Value Count Frequency (%)
3111
16.4%
e 1541
8.1%
t 1018
5.4%
a 977
5.2%
o 969
5.1%
n 860
4.5%
s 821
4.3%
i 803
4.2%
r 691
3.6%
а 518
2.7%
Other values (119) 7625
40.3%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 15064
79.6%
Space Separator 3111
16.4%
Other Punctuation 391
2.1%
Uppercase Letter 266
1.4%
Control 40
0.2%
Decimal Number 25
0.1%
Dash Punctuation 16
0.1%
Close Punctuation 10
0.1%
Open Punctuation 6
< 0.1%
Final Punctuation 3
< 0.1%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 1541
10.2%
t 1018
6.8%
a 977
6.5%
o 969
6.4%
n 860
5.7%
s 821
5.5%
i 803
5.3%
r 691
4.6%
а 518
3.4%
u 486
3.2%
Other values (58) 6380
42.4%
Uppercase Letter
Value Count Frequency (%)
I 107
40.2%
S 18
6.8%
T 17
6.4%
A 11
4.1%
O 10
3.8%
А 8
3.0%
Т 8
3.0%
M 7
2.6%
E 7
2.6%
W 6
2.3%
Other values (29) 67
25.2%
Other Punctuation
Value Count Frequency (%)
. 146
37.3%
, 111
28.4%
' 51
13.0%
" 40
10.2%
? 15
3.8%
: 12
3.1%
/ 9
2.3%
! 7
1.8%
Decimal Number
Value Count Frequency (%)
2 6
24.0%
3 6
24.0%
1 5
20.0%
0 5
20.0%
9 1
4.0%
5 1
4.0%
4 1
4.0%
Space Separator
Value Count Frequency (%)
3111
100.0%
Control
Value Count Frequency (%)
40
100.0%
Dash Punctuation
Value Count Frequency (%)
- 16
100.0%
Close Punctuation
Value Count Frequency (%)
) 10
100.0%
Open Punctuation
Value Count Frequency (%)
( 6
100.0%
Final Punctuation
Value Count Frequency (%)
’ 3
100.0%
Other Symbol
Value Count Frequency (%)
✨ 2
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 11816
62.4%
Common 3604
19.0%
Cyrillic 3514
18.6%

Most frequent character per script

Latin
Value Count Frequency (%)
e 1541
13.0%
t 1018
8.6%
a 977
8.3%
o 969
8.2%
n 860
7.3%
s 821
6.9%
i 803
6.8%
r 691
5.8%
u 486
4.1%
l 428
3.6%
Other values (48) 3222
27.3%
Cyrillic
Value Count Frequency (%)
а 518
14.7%
л 250
7.1%
э 231
6.6%
у 192
5.5%
н 191
5.4%
х 185
5.3%
и 170
4.8%
г 169
4.8%
о 168
4.8%
д 166
4.7%
Other values (39) 1274
36.3%
Common
Value Count Frequency (%)
3111
86.3%
. 146
4.1%
, 111
3.1%
' 51
1.4%
" 40
1.1%
40
1.1%
- 16
0.4%
? 15
0.4%
: 12
0.3%
) 10
0.3%
Other values (12) 52
1.4%

Most occurring blocks

Value Count Frequency (%)
ASCII 15371
81.2%
Cyrillic 3514
18.6%
None 44
0.2%
Punctuation 3
< 0.1%
Dingbats 2
< 0.1%

Most frequent character per block

ASCII
Value Count Frequency (%)
3111
20.2%
e 1541
10.0%
t 1018
6.6%
a 977
6.4%
o 969
6.3%
n 860
5.6%
s 821
5.3%
i 803
5.2%
r 691
4.5%
u 486
3.2%
Other values (58) 4094
26.6%
Cyrillic
Value Count Frequency (%)
а 518
14.7%
л 250
7.1%
э 231
6.6%
у 192
5.5%
н 191
5.4%
х 185
5.3%
и 170
4.8%
г 169
4.8%
о 168
4.8%
д 166
4.7%
Other values (39) 1274
36.3%
None
Value Count Frequency (%)
í 14
31.8%
ó 8
18.2%
à 7
15.9%
ù 4
9.1%
é 3
6.8%
ò 2
4.5%
è 2
4.5%
á 2
4.5%
ú 1
2.3%
ì 1
2.3%
Punctuation
Value Count Frequency (%)
’ 3
100.0%
Dingbats
Value Count Frequency (%)
✨ 2
100.0%

attribute
Categorical

HIGH CORRELATION
MISSING

1

Distinct 3
Distinct (%) 2.4%
Missing 64
Missing (%) 33.9%
Memory size 1.6 KiB
incentives_and_badges
65
badges_only
37
badges
23

Length

Max length 21
Median length 21
Mean length 15.28
Min length 6

Characters and Unicode

Total characters 1910
Distinct characters 15
Distinct categories 2 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 0 ?
Unique (%) 0.0%

Sample

1st row badges
2nd row badges
3rd row badges
4th row incentives_and_badges
5th row badges

Common Values

Value Count Frequency (%)
incentives_and_badges 65
34.4%
badges_only 37
19.6%
badges 23
12.2%
(Missing) 64
33.9%

Length

2022-07-04T20:14:57.354056 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:57.604627 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
incentives_and_badges 65
52.0%
badges_only 37
29.6%
badges 23
18.4%

Most occurring characters

Value Count Frequency (%)
e 255
13.4%
n 232
12.1%
s 190
9.9%
a 190
9.9%
d 190
9.9%
_ 167
8.7%
i 130
6.8%
b 125
6.5%
g 125
6.5%
c 65
3.4%
Other values (5) 241
12.6%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 1743
91.3%
Connector Punctuation 167
8.7%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
e 255
14.6%
n 232
13.3%
s 190
10.9%
a 190
10.9%
d 190
10.9%
i 130
7.5%
b 125
7.2%
g 125
7.2%
c 65
3.7%
t 65
3.7%
Other values (4) 176
10.1%
Connector Punctuation
Value Count Frequency (%)
_ 167
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 1743
91.3%
Common 167
8.7%

Most frequent character per script

Latin
Value Count Frequency (%)
e 255
14.6%
n 232
13.3%
s 190
10.9%
a 190
10.9%
d 190
10.9%
i 130
7.5%
b 125
7.2%
g 125
7.2%
c 65
3.7%
t 65
3.7%
Other values (4) 176
10.1%
Common
Value Count Frequency (%)
_ 167
100.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1910
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
e 255
13.4%
n 232
12.1%
s 190
9.9%
a 190
9.9%
d 190
9.9%
_ 167
8.7%
i 130
6.8%
b 125
6.5%
g 125
6.5%
c 65
3.4%
Other values (5) 241
12.6%

startdate
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING
UNIFORM

Date started

Distinct 95
Distinct (%) 100.0%
Missing 94
Missing (%) 49.7%
Memory size 1.6 KiB
2021-06-18 11:18:11
1
2021-03-28 19:27:53
1
2021-03-28 19:06:41
1
2021-03-28 18:54:34
1
2021-03-28 18:50:54
1
Other values (90)
90

Length

Max length 19
Median length 19
Mean length 19
Min length 19

Characters and Unicode

Total characters 1805
Distinct characters 13
Distinct categories 4 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 95 ?
Unique (%) 100.0%

Sample

1st row 2021-06-18 11:18:11
2nd row 2021-06-18 11:20:53
3rd row 2021-06-18 11:21:29
4th row 2021-06-18 11:21:58
5th row 2021-06-18 11:22:04

Common Values

Value Count Frequency (%)
2021-06-18 11:18:11 1
0.5%
2021-03-28 19:27:53 1
0.5%
2021-03-28 19:06:41 1
0.5%
2021-03-28 18:54:34 1
0.5%
2021-03-28 18:50:54 1
0.5%
2021-03-28 16:38:06 1
0.5%
2021-03-28 15:33:11 1
0.5%
2021-03-28 15:11:50 1
0.5%
2021-03-28 14:36:24 1
0.5%
2021-03-28 13:44:22 1
0.5%
Other values (85) 85
45.0%
(Missing) 94
49.7%

Length

2022-07-04T20:14:57.819122 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
Value Count Frequency (%)
2021-06-18 29
15.3%
2021-03-27 12
6.3%
2021-03-29 12
6.3%
2021-03-28 11
5.8%
2021-06-28 7
3.7%
2021-03-31 6
3.2%
2021-03-26 4
2.1%
2021-06-30 3
1.6%
2021-04-01 2
1.1%
2021-03-30 2
1.1%
Other values (102) 102
53.7%

Most occurring characters

Value Count Frequency (%)
2 316
17.5%
1 287
15.9%
0 272
15.1%
- 190
10.5%
: 190
10.5%
3 125
6.9%
95
5.3%
8 76
4.2%
4 65
3.6%
6 62
3.4%
Other values (3) 127
7.0%

Most occurring categories

Value Count Frequency (%)
Decimal Number 1330
73.7%
Dash Punctuation 190
10.5%
Other Punctuation 190
10.5%
Space Separator 95
5.3%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
2 316
23.8%
1 287
21.6%
0 272
20.5%
3 125
9.4%
8 76
5.7%
4 65
4.9%
6 62
4.7%
5 47
3.5%
9 46
3.5%
7 34
2.6%
Dash Punctuation
Value Count Frequency (%)
- 190
100.0%
Other Punctuation
Value Count Frequency (%)
: 190
100.0%
Space Separator
Value Count Frequency (%)
95
100.0%

Most occurring scripts

Value Count Frequency (%)
Common 1805
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
2 316
17.5%
1 287
15.9%
0 272
15.1%
- 190
10.5%
: 190
10.5%
3 125
6.9%
95
5.3%
8 76
4.2%
4 65
3.6%
6 62
3.4%
Other values (3) 127
7.0%

Most occurring blocks

Value Count Frequency (%)
ASCII 1805
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
2 316
17.5%
1 287
15.9%
0 272
15.1%
- 190
10.5%
: 190
10.5%
3 125
6.9%
95
5.3%
8 76
4.2%
4 65
3.6%
6 62
3.4%
Other values (3) 127
7.0%

datestamp
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING
UNIFORM

Date last action

Distinct 95
Distinct (%) 100.0%
Missing 94
Missing (%) 49.7%
Memory size 1.6 KiB
2021-06-18 11:21:35
1
2021-03-28 19:35:01
1
2021-03-28 19:18:55
1
2021-03-28 19:02:32
1
2021-03-28 18:57:14
1
Other values (90)
90

Length

Max length 19
Median length 19
Mean length 19
Min length 19

Characters and Unicode

Total characters 1805
Distinct characters 13
Distinct categories 4 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 95 ?
Unique (%) 100.0%

Sample

1st row 2021-06-18 11:21:35
2nd row 2021-06-18 11:25:43
3rd row 2021-06-18 11:30:03
4th row 2021-06-18 11:31:07
5th row 2021-06-18 11:26:35

Common Values

Value Count Frequency (%)
2021-06-18 11:21:35 1
0.5%
2021-03-28 19:35:01 1
0.5%
2021-03-28 19:18:55 1
0.5%
2021-03-28 19:02:32 1
0.5%
2021-03-28 18:57:14 1
0.5%
2021-03-28 16:47:42 1
0.5%
2021-03-28 15:35:00 1
0.5%
2021-03-28 15:19:35 1
0.5%
2021-03-28 14:43:33 1
0.5%
2021-03-28 13:55:41 1
0.5%
Other values (85) 85
45.0%
(Missing) 94
49.7%

Length

2022-07-04T20:14:58.019667 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
Value Count Frequency (%)
2021-06-18 29
15.3%
2021-03-27 12
6.3%
2021-03-29 12
6.3%
2021-03-28 11
5.8%
2021-06-28 7
3.7%
2021-03-31 6
3.2%
2021-03-26 4
2.1%
2021-06-30 3
1.6%
2021-04-01 2
1.1%
2021-03-30 2
1.1%
Other values (102) 102
53.7%

Most occurring characters

Value Count Frequency (%)
2 308
17.1%
1 293
16.2%
0 263
14.6%
- 190
10.5%
: 190
10.5%
3 126
7.0%
95
5.3%
8 74
4.1%
6 67
3.7%
5 63
3.5%
Other values (3) 136
7.5%

Most occurring categories

Value Count Frequency (%)
Decimal Number 1330
73.7%
Dash Punctuation 190
10.5%
Other Punctuation 190
10.5%
Space Separator 95
5.3%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
2 308
23.2%
1 293
22.0%
0 263
19.8%
3 126
9.5%
8 74
5.6%
6 67
5.0%
5 63
4.7%
4 63
4.7%
9 39
2.9%
7 34
2.6%
Dash Punctuation
Value Count Frequency (%)
- 190
100.0%
Other Punctuation
Value Count Frequency (%)
: 190
100.0%
Space Separator
Value Count Frequency (%)
95
100.0%

Most occurring scripts

Value Count Frequency (%)
Common 1805
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
2 308
17.1%
1 293
16.2%
0 263
14.6%
- 190
10.5%
: 190
10.5%
3 126
7.0%
95
5.3%
8 74
4.1%
6 67
3.7%
5 63
3.5%
Other values (3) 136
7.5%

Most occurring blocks

Value Count Frequency (%)
ASCII 1805
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
2 308
17.1%
1 293
16.2%
0 263
14.6%
- 190
10.5%
: 190
10.5%
3 126
7.0%
95
5.3%
8 74
4.1%
6 67
3.7%
5 63
3.5%
Other values (3) 136
7.5%

ipaddr
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING
UNIFORM

IP address

Distinct 86
Distinct (%) 90.5%
Missing 94
Missing (%) 49.7%
Memory size 1.6 KiB
78.150.114.59
4
89.35.196.223
2
93.34.237.108
2
193.205.210.75
2
109.246.2.234
2
Other values (81)
83

Length

Max length 14
Median length 13
Mean length 12.35789474
Min length 10

Characters and Unicode

Total characters 1174
Distinct characters 11
Distinct categories 2 ?
Distinct scripts 1 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 79 ?
Unique (%) 83.2%

Sample

1st row 93.34.237.220
2nd row 93.43.213.136
3rd row 37.159.48.7
4th row 79.44.104.179
5th row 37.163.147.26

Common Values

Value Count Frequency (%)
78.150.114.59 4
2.1%
89.35.196.223 2
1.1%
93.34.237.108 2
1.1%
193.205.210.75 2
1.1%
109.246.2.234 2
1.1%
165120149191 2
1.1%
95.151.3.140 2
1.1%
158143227247 1
0.5%
86.22.46.119 1
0.5%
82.37.13.183 1
0.5%
Other values (76) 76
40.2%
(Missing) 94
49.7%

Length

2022-07-04T20:14:58.253485 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
Value Count Frequency (%)
78.150.114.59 4
4.2%
93.34.237.108 2
2.1%
193.205.210.75 2
2.1%
109.246.2.234 2
2.1%
165120149191 2
2.1%
95.151.3.140 2
2.1%
89.35.196.223 2
2.1%
80.183.24.189 1
1.1%
79.44.104.179 1
1.1%
37.163.147.26 1
1.1%
Other values (76) 76
80.0%

Most occurring characters

Value Count Frequency (%)
. 231
19.7%
1 211
18.0%
2 126
10.7%
3 103
8.8%
5 88
7.5%
4 82
7.0%
9 82
7.0%
8 80
6.8%
7 65
5.5%
0 57
4.9%

Most occurring categories

Value Count Frequency (%)
Decimal Number 943
80.3%
Other Punctuation 231
19.7%

Most frequent character per category

Decimal Number
Value Count Frequency (%)
1 211
22.4%
2 126
13.4%
3 103
10.9%
5 88
9.3%
4 82
8.7%
9 82
8.7%
8 80
8.5%
7 65
6.9%
0 57
6.0%
6 49
5.2%
Other Punctuation
Value Count Frequency (%)
. 231
100.0%

Most occurring scripts

Value Count Frequency (%)
Common 1174
100.0%

Most frequent character per script

Common
Value Count Frequency (%)
. 231
19.7%
1 211
18.0%
2 126
10.7%
3 103
8.8%
5 88
7.5%
4 82
7.0%
9 82
7.0%
8 80
6.8%
7 65
5.5%
0 57
4.9%

Most occurring blocks

Value Count Frequency (%)
ASCII 1174
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
. 231
19.7%
1 211
18.0%
2 126
10.7%
3 103
8.8%
5 88
7.5%
4 82
7.0%
9 82
7.0%
8 80
6.8%
7 65
5.5%
0 57
4.9%

refurl
Categorical

HIGH CORRELATION
MISSING

Referrer URL

Distinct 4
Distinct (%) 17.4%
Missing 166
Missing (%) 87.8%
Memory size 1.6 KiB
android-app://com.google.android.gm/
20
https://mail.google.com/
1
https://wenet.limequery.com/368194?token=SCnk80C72GIvYdj&lang=it
1
https://wenet.limequery.com/772946
1

Length

Max length 64
Median length 36
Mean length 36.60869565
Min length 24

Characters and Unicode

Total characters 842
Distinct characters 43
Distinct categories 6 ?
Distinct scripts 2 ?
Distinct blocks 1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique 3 ?
Unique (%) 13.0%

Sample

1st row android-app://com.google.android.gm/
2nd row android-app://com.google.android.gm/
3rd row android-app://com.google.android.gm/
4th row https://mail.google.com/
5th row android-app://com.google.android.gm/

Common Values

Value Count Frequency (%)
android-app://com.google.android.gm/ 20
10.6%
https://mail.google.com/ 1
0.5%
https://wenet.limequery.com/368194?token=SCnk80C72GIvYdj&lang=it 1
0.5%
https://wenet.limequery.com/772946 1
0.5%
(Missing) 166
87.8%

Length

2022-07-04T20:14:58.522114 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-04T20:14:58.784044 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Value Count Frequency (%)
android-app://com.google.android.gm 20
87.0%
https://mail.google.com 1
4.3%
https://wenet.limequery.com/368194?token=scnk80c72givydj&lang=it 1
4.3%
https://wenet.limequery.com/772946 1
4.3%

Most occurring characters

Value Count Frequency (%)
o 106
12.6%
d 81
9.6%
/ 69
8.2%
. 66
7.8%
g 63
7.5%
a 62
7.4%
m 46
5.5%
n 45
5.3%
i 44
5.2%
p 43
5.1%
Other values (33) 217
25.8%

Most occurring categories

Value Count Frequency (%)
Lowercase Letter 638
75.8%
Other Punctuation 160
19.0%
Dash Punctuation 20
2.4%
Decimal Number 16
1.9%
Uppercase Letter 6
0.7%
Math Symbol 2
0.2%

Most frequent character per category

Lowercase Letter
Value Count Frequency (%)
o 106
16.6%
d 81
12.7%
g 63
9.9%
a 62
9.7%
m 46
7.2%
n 45
7.1%
i 44
6.9%
p 43
6.7%
r 42
6.6%
e 30
4.7%
Other values (12) 76
11.9%
Decimal Number
Value Count Frequency (%)
7 3
18.8%
2 2
12.5%
9 2
12.5%
4 2
12.5%
8 2
12.5%
6 2
12.5%
1 1
6.2%
3 1
6.2%
0 1
6.2%
Other Punctuation
Value Count Frequency (%)
/ 69
43.1%
. 66
41.2%
: 23
14.4%
? 1
0.6%
& 1
0.6%
Uppercase Letter
Value Count Frequency (%)
C 2
33.3%
S 1
16.7%
G 1
16.7%
I 1
16.7%
Y 1
16.7%
Dash Punctuation
Value Count Frequency (%)
- 20
100.0%
Math Symbol
Value Count Frequency (%)
= 2
100.0%

Most occurring scripts

Value Count Frequency (%)
Latin 644
76.5%
Common 198
23.5%

Most frequent character per script

Latin
Value Count Frequency (%)
o 106
16.5%
d 81
12.6%
g 63
9.8%
a 62
9.6%
m 46
7.1%
n 45
7.0%
i 44
6.8%
p 43
6.7%
r 42
6.5%
e 30
4.7%
Other values (17) 82
12.7%
Common
Value Count Frequency (%)
/ 69
34.8%
. 66
33.3%
: 23
11.6%
- 20
10.1%
7 3
1.5%
2 2
1.0%
9 2
1.0%
= 2
1.0%
4 2
1.0%
8 2
1.0%
Other values (6) 7
3.5%

Most occurring blocks

Value Count Frequency (%)
ASCII 842
100.0%

Most frequent character per block

ASCII
Value Count Frequency (%)
o 106
12.6%
d 81
9.6%
/ 69
8.2%
. 66
7.8%
g 63
7.5%
a 62
7.4%
m 46
5.5%
n 45
5.3%
i 44
5.2%
p 43
5.1%
Other values (33) 217
25.8%

interviewtime
Real number (ℝ ≥0 )

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Total time

Distinct 89
Distinct (%) 93.7%
Missing 94
Missing (%) 49.7%
Infinite 0
Infinite (%) 0.0%
Mean 366.6412632
Minimum 0
Maximum 3322.28
Zeros 7
Zeros (%) 3.7%
Negative 0
Negative (%) 0.0%
Memory size 1.6 KiB
2022-07-04T20:14:59.059624 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum 0
5-th percentile 0
Q1 221.545
median 310.95
Q3 451.66
95-th percentile 698.571
Maximum 3322.28
Range 3322.28
Interquartile range (IQR) 230.115

Descriptive statistics

Standard deviation 372.1372226
Coefficient of variation (CV) 1.014990019
Kurtosis 42.59954668
Mean 366.6412632
Median Absolute Deviation (MAD) 125.65
Skewness 5.533978278
Sum 34830.92
Variance 138486.1124
Monotonicity Not monotonic
2022-07-04T20:14:59.349654 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
0 7
3.7%
429.69 1
0.5%
481.24 1
0.5%
382.04 1
0.5%
578.42 1
0.5%
111.95 1
0.5%
469.43 1
0.5%
431.82 1
0.5%
682.26 1
0.5%
429.31 1
0.5%
Other values (79) 79
41.8%
(Missing) 94
49.7%
Value Count Frequency (%)
0 7
3.7%
3.04 1
0.5%
4.07 1
0.5%
27.97 1
0.5%
49.07 1
0.5%
104.28 1
0.5%
111.95 1
0.5%
120.21 1
0.5%
121.55 1
0.5%
126.48 1
0.5%
Value Count Frequency (%)
3322.28 1
0.5%
1254.31 1
0.5%
847.22 1
0.5%
762.21 1
0.5%
736.63 1
0.5%
682.26 1
0.5%
665.63 1
0.5%
657.1 1
0.5%
614.71 1
0.5%
609.49 1
0.5%

groupTime388
Real number (ℝ ≥0 )

HIGH CORRELATION
MISSING

Group time: User experience - part 1

Distinct 87
Distinct (%) 98.9%
Missing 101
Missing (%) 53.4%
Infinite 0
Infinite (%) 0.0%
Mean 32.26170455
Minimum 3.04
Maximum 233.22
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 1.6 KiB
2022-07-04T20:14:59.653923 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum 3.04
5-th percentile 13.624
Q1 19.9425
median 24.33
Q3 32.81
95-th percentile 63.4875
Maximum 233.22
Range 230.18
Interquartile range (IQR) 12.8675

Descriptive statistics

Standard deviation 32.27423944
Coefficient of variation (CV) 1.000388538
Kurtosis 24.89099743
Mean 32.26170455
Median Absolute Deviation (MAD) 5.97
Skewness 4.687234776
Sum 2839.03
Variance 1041.626532
Monotonicity Not monotonic
2022-07-04T20:14:59.938316 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
26.02 2
1.1%
24.32 1
0.5%
233.22 1
0.5%
14.69 1
0.5%
20.05 1
0.5%
67.25 1
0.5%
16.21 1
0.5%
34.18 1
0.5%
22.79 1
0.5%
50.31 1
0.5%
Other values (77) 77
40.7%
(Missing) 101
53.4%
Value Count Frequency (%)
3.04 1
0.5%
4.07 1
0.5%
8.43 1
0.5%
10.82 1
0.5%
13.05 1
0.5%
14.69 1
0.5%
14.76 1
0.5%
14.81 1
0.5%
15.71 1
0.5%
15.88 1
0.5%
Value Count Frequency (%)
233.22 1
0.5%
198.56 1
0.5%
121.55 1
0.5%
69.24 1
0.5%
67.25 1
0.5%
56.5 1
0.5%
52.08 1
0.5%
50.31 1
0.5%
49.07 1
0.5%
47.77 1
0.5%

groupTime382
Real number (ℝ ≥0 )

HIGH CORRELATION
MISSING

Group time: Location

Distinct 82
Distinct (%) 98.8%
Missing 106
Missing (%) 56.1%
Infinite 0
Infinite (%) 0.0%
Mean 69.81493976
Minimum 23.03
Maximum 565.71
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 1.6 KiB
2022-07-04T20:15:00.222736 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum 23.03
5-th percentile 34.278
Q1 44.66
median 59.69
Q3 79.87
95-th percentile 119.325
Maximum 565.71
Range 542.68
Interquartile range (IQR) 35.21

Descriptive statistics

Standard deviation 60.40573207
Coefficient of variation (CV) 0.865226444
Kurtosis 56.59170529
Mean 69.81493976
Median Absolute Deviation (MAD) 15.33
Skewness 6.936510904
Sum 5794.64
Variance 3648.852467
Monotonicity Not monotonic
2022-07-04T20:15:00.533169 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
40.53 2
1.1%
58.85 1
0.5%
149.51 1
0.5%
56.59 1
0.5%
65.21 1
0.5%
27.88 1
0.5%
60.96 1
0.5%
70.3 1
0.5%
83.93 1
0.5%
81.26 1
0.5%
Other values (72) 72
38.1%
(Missing) 106
56.1%
Value Count Frequency (%)
23.03 1
0.5%
25.82 1
0.5%
27.88 1
0.5%
33.49 1
0.5%
34.23 1
0.5%
34.71 1
0.5%
36.68 1
0.5%
37.42 1
0.5%
39.1 1
0.5%
39.28 1
0.5%
Value Count Frequency (%)
565.71 1
0.5%
149.51 1
0.5%
129.8 1
0.5%
128.44 1
0.5%
119.84 1
0.5%
114.69 1
0.5%
108.49 1
0.5%
107.34 1
0.5%
92.92 1
0.5%
90.15 1
0.5%

groupTime383
Real number (ℝ ≥0 )

HIGH CORRELATION
HIGH CORRELATION
MISSING

Group time: Time and space of questions

Distinct 83
Distinct (%) 100.0%
Missing 106
Missing (%) 56.1%
Infinite 0
Infinite (%) 0.0%
Mean 31.87192771
Minimum 9.65
Maximum 224.17
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 1.6 KiB
2022-07-04T20:15:01.103234 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum 9.65
5-th percentile 14.163
Q1 18.04
median 23.45
Q3 29.765
95-th percentile 81.942
Maximum 224.17
Range 214.52
Interquartile range (IQR) 11.725

Descriptive statistics

Standard deviation 33.07645513
Coefficient of variation (CV) 1.037792738
Kurtosis 20.47456932
Mean 31.87192771
Median Absolute Deviation (MAD) 5.88
Skewness 4.297316531
Sum 2645.37
Variance 1094.051884
Monotonicity Not monotonic
2022-07-04T20:15:01.392614 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
26.51 1
0.5%
29.44 1
0.5%
45.36 1
0.5%
193.09 1
0.5%
26.34 1
0.5%
18.25 1
0.5%
9.65 1
0.5%
29.82 1
0.5%
24.87 1
0.5%
41.21 1
0.5%
Other values (73) 73
38.6%
(Missing) 106
56.1%
Value Count Frequency (%)
9.65 1
0.5%
12.83 1
0.5%
13.12 1
0.5%
13.51 1
0.5%
14.13 1
0.5%
14.46 1
0.5%
14.94 1
0.5%
15.17 1
0.5%
15.48 1
0.5%
15.49 1
0.5%
Value Count Frequency (%)
224.17 1
0.5%
193.09 1
0.5%
128.52 1
0.5%
89.29 1
0.5%
84.64 1
0.5%
57.66 1
0.5%
53.57 1
0.5%
47.64 1
0.5%
45.36 1
0.5%
43.8 1
0.5%

groupTime384
Real number (ℝ ≥0 )

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Group time: Badges

Distinct 80
Distinct (%) 98.8%
Missing 108
Missing (%) 57.1%
Infinite 0
Infinite (%) 0.0%
Mean 141.9385185
Minimum 29.63
Maximum 3197.9
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 1.6 KiB
2022-07-04T20:15:01.691321 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum 29.63
5-th percentile 33.14
Q1 54.07
median 90.96
Q3 138.61
95-th percentile 217.27
Maximum 3197.9
Range 3168.27
Interquartile range (IQR) 84.54

Descriptive statistics

Standard deviation 352.5288402
Coefficient of variation (CV) 2.483672817
Kurtosis 73.0182888
Mean 141.9385185
Median Absolute Deviation (MAD) 39.12
Skewness 8.373555049
Sum 11497.02
Variance 124276.5831
Monotonicity Not monotonic
2022-07-04T20:15:01.970484 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
91.7 2
1.1%
109.05 1
0.5%
54.07 1
0.5%
197.46 1
0.5%
148.23 1
0.5%
171.24 1
0.5%
92.42 1
0.5%
29.63 1
0.5%
71.88 1
0.5%
187.37 1
0.5%
Other values (70) 70
37.0%
(Missing) 108
57.1%
Value Count Frequency (%)
29.63 1
0.5%
31.69 1
0.5%
32.23 1
0.5%
32.81 1
0.5%
33.14 1
0.5%
36.21 1
0.5%
38.97 1
0.5%
40.04 1
0.5%
40.45 1
0.5%
40.74 1
0.5%
Value Count Frequency (%)
3197.9 1
0.5%
622.17 1
0.5%
242.46 1
0.5%
231.89 1
0.5%
217.27 1
0.5%
211.45 1
0.5%
197.46 1
0.5%
190.89 1
0.5%
187.37 1
0.5%
171.24 1
0.5%

groupTime385
Real number (ℝ ≥0 )

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Group time: Messages

Distinct 39
Distinct (%) 100.0%
Missing 150
Missing (%) 79.4%
Infinite 0
Infinite (%) 0.0%
Mean 85.35282051
Minimum 23.48
Maximum 195.63
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 1.6 KiB
2022-07-04T20:15:02.257032 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum 23.48
5-th percentile 40.726
Q1 53.16
median 75.53
Q3 112.885
95-th percentile 169.027
Maximum 195.63
Range 172.15
Interquartile range (IQR) 59.725

Descriptive statistics

Standard deviation 43.46219155
Coefficient of variation (CV) 0.5092062722
Kurtosis -0.03285073085
Mean 85.35282051
Median Absolute Deviation (MAD) 22.95
Skewness 0.9224367957
Sum 3328.76
Variance 1888.962094
Monotonicity Not monotonic
2022-07-04T20:15:02.521555 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
Value Count Frequency (%)
106.69 1
0.5%
86.35 1
0.5%
41.73 1
0.5%
58.48 1
0.5%
162.27 1
0.5%
64.95 1
0.5%
56.83 1
0.5%
79.85 1
0.5%
125.02 1
0.5%
59.16 1
0.5%
Other values (29) 29
15.3%
(Missing) 150
79.4%
Value Count Frequency (%)
23.48 1
0.5%
33.85 1
0.5%
41.49 1
0.5%
41.73 1
0.5%
43.5 1
0.5%
43.84 1
0.5%
49.9 1
0.5%
50.78 1
0.5%
52.58 1
0.5%
53.13 1
0.5%
Value Count Frequency (%)
195.63 1
0.5%
172.69 1
0.5%
168.62 1
0.5%
162.27 1
0.5%
152.49 1
0.5%
138.98 1
0.5%
128.23 1
0.5%
125.02 1
0.5%
124.52 1
0.5%
119.08 1
0.5%

groupTime386
Real number (ℝ ≥0 )

HIGH CORRELATION
MISSING

Group time: User experience - part 2

Distinct 78
Distinct (%) 98.7%
Missing 110
Missing (%) 58.2%
Infinite 0
Infinite (%) 0.0%
Mean 63.58063291
Minimum 19.73
Maximum 181.19
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 1.6 KiB
2022-07-04T20:15:02.815007 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum 19.73
5-th percentile 28.21
Q1 45.455
median 55.32
Q3 75.02
95-th percentile 113.384
Maximum 181.19
Range 161.46
Interquartile range (IQR) 29.565

Descriptive statistics

Standard deviation 28.67548168
Coefficient of variation (CV) 0.4510096922
Kurtosis 2.842053835
Mean 63.58063291
Median Absolute Deviation (MAD) 11.79
Skewness 1.41505989
Sum 5022.87
Variance 822.2832496
Monotonicity Not monotonic
2022-07-04T20:15:03.102878 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
45.7 2
1.1%
61.6 1
0.5%
59.67 1
0.5%
94.28 1
0.5%
24.56 1
0.5%
104.06 1
0.5%
63.97 1
0.5%
28.58 1
0.5%
84.82 1
0.5%
94.76 1
0.5%
Other values (68) 68
36.0%
(Missing) 110
58.2%
Value Count Frequency (%)
19.73 1
0.5%
21.74 1
0.5%
24.56 1
0.5%
24.88 1
0.5%
28.58 1
0.5%
31.55 1
0.5%
33.7 1
0.5%
34.21 1
0.5%
39.49 1
0.5%
40.14 1
0.5%
Value Count Frequency (%)
181.19 1
0.5%
139.77 1
0.5%
121.36 1
0.5%
115.13 1
0.5%
113.19 1
0.5%
111.57 1
0.5%
109 1
0.5%
108.17 1
0.5%
104.06 1
0.5%
100.14 1
0.5%

groupTime387
Real number (ℝ ≥0 )

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Group time: Final question

Distinct 77
Distinct (%) 98.7%
Missing 111
Missing (%) 58.7%
Infinite 0
Infinite (%) 0.0%
Mean 47.47730769
Minimum 1.6
Maximum 271.32
Zeros 0
Zeros (%) 0.0%
Negative 0
Negative (%) 0.0%
Memory size 1.6 KiB
2022-07-04T20:15:03.422708 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum 1.6
5-th percentile 2.587
Q1 4.9625
median 7.1
Q3 59.5225
95-th percentile 208.1295
Maximum 271.32
Range 269.72
Interquartile range (IQR) 54.56

Descriptive statistics

Standard deviation 74.0592278
Coefficient of variation (CV) 1.559886847
Kurtosis 1.582226958
Mean 47.47730769
Median Absolute Deviation (MAD) 3.1
Skewness 1.689750235
Sum 3703.23
Variance 5484.769223
Monotonicity Not monotonic
2022-07-04T20:15:03.730636 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)
4.49 2
1.1%
128.83 1
0.5%
221.25 1
0.5%
16.8 1
0.5%
3.31 1
0.5%
3.76 1
0.5%
271.32 1
0.5%
122.82 1
0.5%
31.73 1
0.5%
89.12 1
0.5%
Other values (67) 67
35.4%
(Missing) 111
58.7%
Value Count Frequency (%)
1.6 1
0.5%
2.39 1
0.5%
2.46 1
0.5%
2.57 1
0.5%
2.59 1
0.5%
3.31 1
0.5%
3.73 1
0.5%
3.76 1
0.5%
3.99 1
0.5%
4.01 1
0.5%
Value Count Frequency (%)
271.32 1
0.5%
268.94 1
0.5%
221.25 1
0.5%
218.95 1
0.5%
206.22 1
0.5%
203.78 1
0.5%
200.68 1
0.5%
191.33 1
0.5%
183.37 1
0.5%
182.45 1
0.5%

Interactions

2022-07-04T20:13:53.083768 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:30.016089 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:32.154954 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:34.582445 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:36.916736 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:39.403255 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:41.598325 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:44.009098 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:46.158496 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:48.462836 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:50.850933 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:53.264989 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:30.226871 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:32.547511 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:34.770877 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:37.129749 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:39.594082 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:41.781615 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:44.187082 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:46.355019 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:48.644159 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:51.038917 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:53.461468 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:30.432206 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:32.751484 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:34.978894 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:37.355406 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:39.797140 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:41.999444 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:44.399415 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:46.581062 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:48.849160 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:51.240879 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:53.676337 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:30.620218 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:32.947436 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:35.170932 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:37.570592 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:40.012193 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:42.218439 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:44.614848 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:46.815502 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:49.070117 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:51.473280 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:53.865222 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:30.818490 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:33.151963 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:35.363870 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:37.786069 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:40.214376 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:42.418171 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:44.812232 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:47.028095 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:49.457449 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:51.676645 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:54.055499 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:30.999281 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:33.338259 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:35.572149 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:38.189725 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:40.401160 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:42.601336 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:44.991774 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:47.220581 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:49.658013 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:51.868423 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:54.248095 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:31.190533 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:33.539835 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:35.788060 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:38.388350 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:40.593520 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:42.801708 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:45.179039 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:47.424875 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:49.851048 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:52.070159 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:54.426910 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:31.368302 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:33.738111 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:35.990246 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:38.578243 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:40.783130 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:42.985385 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:45.357937 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:47.617296 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:50.033385 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:52.260031 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:54.809522 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:31.560338 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:33.964350 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:36.216000 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:38.783386 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:40.980217 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:43.188281 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:45.554762 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:47.828113 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:50.249095 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:52.460413 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:55.012801 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:31.756257 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:34.171971 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:36.451191 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:39.000780 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:41.194911 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:43.405653 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:45.752798 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:48.055740 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:50.462242 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:52.665177 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:55.227023 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:31.966068 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:34.385756 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:36.695782 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:39.209970 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:41.410884 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:43.818272 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:45.970899 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:48.276090 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:50.660283 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
2022-07-04T20:13:52.888489 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-07-04T20:15:03.991292 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient ( ρ ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r . It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y , one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-07-04T20:15:04.412632 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient ( r ) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r .

To calculate r for two variables X and Y , one divides the covariance of X and Y by the product of their standard deviations.
2022-07-04T20:15:04.824327 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient ( τ ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y , one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-07-04T20:15:05.468130 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here .

Missing values

2022-07-04T20:13:56.172292 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-07-04T20:14:01.343610 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-07-04T20:14:14.305719 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.